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a/helm-frontend/package.json +++ b/helm-frontend/package.json @@ -31,14 +31,14 @@ "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^14.3.1", "@testing-library/user-event": "^14.6.1", - "@types/node": "^25.2.3", + "@types/node": "^25.3.0", "@types/react": "^18.2.15", "@types/react-dom": "^18.2.7", "@types/react-helmet": "^6.1.11", "@typescript-eslint/eslint-plugin": "^6.21.0", "@typescript-eslint/parser": "^6.21.0", "@vitejs/plugin-react": "^4.7.0", - "autoprefixer": "^10.4.24", + "autoprefixer": "^10.4.27", "daisyui": "^3.9.4", "eslint": "^8.45.0", "eslint-config-prettier": "^9.1.2", diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index 63a9dc21d54..e57212683f0 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -1508,12 +1508,12 @@ resolved "https://registry.yarnpkg.com/@types/ms/-/ms-0.7.32.tgz#f6cd08939ae3ad886fcc92ef7f0109dacddf61ab" integrity 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src/helm/config/model_metadata.yaml | 53 ++++++++++++++++++++++++++ 2 files changed, 83 insertions(+) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index f8db2452e88..d032ecc1263 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -2572,6 +2572,36 @@ model_deployments: client_spec: class_name: "helm.clients.mistral_client.MistralAIClient" + - name: mistralai/mistral-large-2512 + model_name: mistralai/mistral-large-2512 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + + - name: mistralai/mistral-medium-2508 + model_name: mistralai/mistral-medium-2508 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + + - name: mistralai/mistral-small-2506 + model_name: mistralai/mistral-small-2506 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + + - name: mistralai/ministral-14b-2512 + model_name: mistralai/ministral-14b-2512 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + + - name: mistralai/ministral-8b-2512 + model_name: mistralai/ministral-8b-2512 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + + - name: mistralai/ministral-3b-2512 + model_name: mistralai/ministral-3b-2512 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + - name: mistralai/open-mistral-nemo-2407 model_name: mistralai/open-mistral-nemo-2407 tokenizer_name: mistralai/Mistral-Nemo-Base-2407 diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index ade08c21de5..36c8e866734 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -2771,6 +2771,59 @@ models: release_date: 2024-11-18 tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: mistralai/mistral-large-2512 + display_name: Mistral Large 3 (2512) + description: Mistral Large 3 is a open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture with 41B active parameters and 675B total parameters. ([doc](https://docs.mistral.ai/models/mistral-large-3-25-12), [blog](https://mistral.ai/news/mistral-3)) + creator_organization_name: Mistral AI + access: open + num_parameters: 675000000000 + release_date: 2025-12-02 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: mistralai/mistral-medium-2508 + display_name: Mistral Medium 3.1 (2508) + description: Mistral Medium 3.1 is a Mistral multimodal model. ([doc](https://docs.mistral.ai/models/mistral-medium-3-1-25-08)) + creator_organization_name: Mistral AI + access: limited + release_date: 2025-08-12 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: mistralai/mistral-small-2506 + display_name: Mistral Small 3.2 (2506) + description: Mistral Small 3.2 is a Mistral multimodal model. ([doc](https://docs.mistral.ai/models/mistral-small-3-2-25-06)) + creator_organization_name: Mistral AI + access: open + num_parameters: 24000000000 + release_date: 2025-06-20 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: mistralai/ministral-14b-2512 + display_name: Ministral 3 (14B 2512) + description: Ministral 3 is a family of parameter-efficient dense language models designed for compute and memory constrained applications. ([doc](https://docs.mistral.ai/models/ministral-3-14b-25-12), [blog](https://mistral.ai/news/mistral-3), [paper](https://arxiv.org/abs/2601.08584)) + creator_organization_name: Mistral AI + access: open + num_parameters: 14000000000 + release_date: 2025-12-02 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: mistralai/ministral-8b-2512 + display_name: Ministral 3 (8B 2512) + description: Ministral 3 is a family of parameter-efficient dense language models designed for compute and memory constrained applications. ([doc](https://docs.mistral.ai/models/ministral-3-8b-25-12), [blog](https://mistral.ai/news/mistral-3), [paper](https://arxiv.org/abs/2601.08584)) + creator_organization_name: Mistral AI + access: open + num_parameters: 8000000000 + release_date: 2025-12-02 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: mistralai/ministral-3b-2512 + display_name: Ministral 3 (3B 2512) + description: Ministral 3 is a family of parameter-efficient dense language models designed for compute and memory constrained applications. ([doc](https://docs.mistral.ai/models/ministral-3-3b-25-12), [blog](https://mistral.ai/news/mistral-3), [paper](https://arxiv.org/abs/2601.08584)) + creator_organization_name: Mistral AI + access: open + num_parameters: 3000000000 + release_date: 2025-12-02 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: mistralai/open-mistral-nemo-2407 display_name: Mistral NeMo (2402) description: Mistral NeMo is a multilingual 12B model with a large context window of 128K tokens. ([blog](https://mistral.ai/news/mistral-nemo/)) From 5e5c01001fba9daf8fdfcce24d4d36ba1563a272 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 6 Mar 2026 10:16:36 -0800 Subject: [PATCH 04/89] Add GPT-5.4 (#4099) --- src/helm/config/model_deployments.yaml | 14 ++++++++++++++ src/helm/config/model_metadata.yaml | 16 ++++++++++++++++ 2 files changed, 30 insertions(+) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index d032ecc1263..9ded3285150 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -2866,6 +2866,20 @@ model_deployments: client_spec: class_name: "helm.clients.openai_responses_client.OpenAIResponseClient" + - name: openai/gpt-5.4-pro-2026-03-05 + model_name: openai/gpt-5.4-pro-2026-03-05 + tokenizer_name: openai/o200k_base + max_sequence_length: 1050000 + client_spec: + class_name: "helm.clients.openai_responses_client.OpenAIResponseClient" + + - name: openai/gpt-5.4-2026-03-05 + model_name: openai/gpt-5.4-2026-03-05 + tokenizer_name: openai/o200k_base + max_sequence_length: 1050000 + client_spec: + class_name: "helm.clients.openai_responses_client.OpenAIResponseClient" + - name: openai/gpt-5.2-2025-12-11 model_name: openai/gpt-5.2-2025-12-11 tokenizer_name: openai/o200k_base diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index 36c8e866734..3f0e3fc37df 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -3318,6 +3318,22 @@ models: release_date: 2025-08-07 tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, OPENAI_CHATGPT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: openai/gpt-5.4-pro-2026-03-05 + display_name: GPT-5.4 Pro (2026-03-05) + description: GPT-5.4 Pro (2026-03-05) is a model in the GPT-5 model family that incorporates recent advances in reasoning, coding, and agentic workflows. ([blog](https://openai.com/index/introducing-gpt-5-4/)) + creator_organization_name: OpenAI + access: limited + release_date: 2026-03-05 + tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, OPENAI_CHATGPT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: openai/gpt-5.4-2026-03-05 + display_name: GPT-5.4 (2026-03-05) + description: GPT-5.4 (2026-03-05) is a model in the GPT-5 model family that incorporates recent advances in reasoning, coding, and agentic workflows. ([blog](https://openai.com/index/introducing-gpt-5-4/)) + creator_organization_name: OpenAI + access: limited + release_date: 2026-03-05 + tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, OPENAI_CHATGPT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: openai/gpt-5.2-2025-12-11 display_name: GPT-5.2 (2025-12-11) description: GPT-5.2 (2025-12-11) is a model in the GPT-5 model family that is intended for coding and agentic tasks across industries. ([blog](https://openai.com/index/introducing-gpt-5-2/)) From c61a7ef3c828796d2bfcef2132bebc8383b05bd2 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 6 Mar 2026 13:35:00 -0800 Subject: [PATCH 05/89] Add model_deployment_generator decorator (#4100) --- .../benchmark/model_deployment_registry.py | 176 +++++++++++------- 1 file changed, 108 insertions(+), 68 deletions(-) diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index add8f00423c..58f18d123c1 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -1,4 +1,4 @@ -from typing import Dict, Optional, List +from typing import Callable, Dict, Optional, List, TypeVar from dataclasses import dataclass import cattrs @@ -123,77 +123,19 @@ def register_model_deployments_from_path(path: str) -> None: register_model_deployment(model_deployment) -def auto_generate_model_deployment(name: str) -> ModelDeployment: - name_parts = name.split("/") - model_deployment_base = name_parts[0] - model_name = "/".join(name_parts[-2:]) - if model_deployment_base == "together": - return ModelDeployment( - name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.together_client.TogetherClient") - ) - elif model_deployment_base == "litellm": - return ModelDeployment( - name=name, - model_name=model_name, - client_spec=ClientSpec( - "helm.clients.litellm_client.LiteLLMCompletionClient", args={"litellm_model": "/".join(name_parts[1:])} - ), - ) - elif model_deployment_base == "huggingface": - from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer - - pretrained_model_name_or_path = "/".join(name_parts[1:]) - with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: - max_sequence_length = tokenizer.model_max_length - if max_sequence_length > 1_000_000_000: - hwarn( - f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " - "input truncation may not work correctly; errors may result from exceeding the model's max length" - ) - return ModelDeployment( - name=name, - model_name=model_name, - client_spec=ClientSpec( - "helm.clients.huggingface_client.HuggingFaceClient", - args={"pretrained_model_name_or_path": pretrained_model_name_or_path}, - ), - tokenizer_name=name, - max_sequence_length=max_sequence_length, - ) - elif model_deployment_base == "huggingface-inference-providers": - from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer - - pretrained_model_name_or_path = "/".join(name_parts[1:]) - pretrained_model_name_or_path = pretrained_model_name_or_path.split(":")[0] - with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: - max_sequence_length = tokenizer.model_max_length - if max_sequence_length > 1_000_000_000: - hwarn( - f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " - "input truncation may not work correctly; errors may result from exceeding the model's max length" - ) - return ModelDeployment( - name=name, - model_name=model_name, - client_spec=ClientSpec( - "helm.clients.huggingface_inference_providers_client.HuggingFaceInferenceProvidersClient" - ), - tokenizer_name=f"huggingface/{pretrained_model_name_or_path}", - max_sequence_length=max_sequence_length, - ) - else: - raise NotImplementedError(f"Unknown model deployment base {model_deployment_base}") +def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeployment: + deployment: Optional[ModelDeployment] = DEPLOYMENT_NAME_TO_MODEL_DEPLOYMENT.get(name) + if not deployment: + for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): + if name.startswith(prefix): + deployment = model_deployment_generator(name) -def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeployment: - if name not in DEPLOYMENT_NAME_TO_MODEL_DEPLOYMENT: - name_parts = name.split("/") - if len(name_parts) > 2: - return auto_generate_model_deployment(name) + if not deployment: raise ValueError(f"Model deployment {name} not found") - deployment: ModelDeployment = DEPLOYMENT_NAME_TO_MODEL_DEPLOYMENT[name] + if deployment.deprecated and warn_deprecated: - hwarn(f"DEPLOYMENT Model deployment {name} is deprecated") + hwarn(f"Model deployment {name} is deprecated") return deployment @@ -277,3 +219,101 @@ def get_default_model_deployment_for_model( # Some models are added but have no deployments yet. # In this case, we return None. return None + + +ModelDeploymentGenerator = Callable[[str], ModelDeployment] +"""A function that takes in a model name and returns a ModelDeployment""" + + +_REGISTERED_MODEL_DEPLOYMENT_GENERATORS: Dict[str, ModelDeploymentGenerator] = {} +"""Dict of prefixes to ModelDeploymentGenerators.""" + + +F = TypeVar("F", bound=ModelDeploymentGenerator) + + +def model_deployment_generator(prefix: str) -> Callable[[F], F]: + """Register the ModelDeploymentGenerator for the given model name prefix.""" + + def wrap(func: F) -> F: + key = prefix.strip("/") + "/" + if key in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS: + raise ValueError(f"A ModelDeploymentGenerator with prefix {key} already exists") + _REGISTERED_MODEL_DEPLOYMENT_GENERATORS[key] = func + return func + + return wrap + + +@model_deployment_generator("together") +def get_together_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.together_client.TogetherClient") + ) + + +@model_deployment_generator("litellm") +def get_litellm_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.litellm_client.LiteLLMCompletionClient", args={"litellm_model": "/".join(name_parts[1:])} + ), + ) + + +@model_deployment_generator("huggingface") +def get_huggingface_model_deployment(name: str): + from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer + + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + pretrained_model_name_or_path = "/".join(name_parts[1:]) + with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: + max_sequence_length = tokenizer.model_max_length + if max_sequence_length > 1_000_000_000: + hwarn( + f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " + "input truncation may not work correctly; errors may result from exceeding the model's max length" + ) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.huggingface_client.HuggingFaceClient", + args={"pretrained_model_name_or_path": pretrained_model_name_or_path}, + ), + tokenizer_name=name, + max_sequence_length=max_sequence_length, + ) + + +@model_deployment_generator("huggingface-inference-providers") +def get_huggingface_inference_providers_model_deployment(name: str): + from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer + + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + pretrained_model_name_or_path = "/".join(name_parts[1:]) + pretrained_model_name_or_path = pretrained_model_name_or_path.split(":")[0] + with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: + max_sequence_length = tokenizer.model_max_length + if max_sequence_length > 1_000_000_000: + hwarn( + f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " + "input truncation may not work correctly; errors may result from exceeding the model's max length" + ) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.huggingface_inference_providers_client.HuggingFaceInferenceProvidersClient" + ), + tokenizer_name=f"huggingface/{pretrained_model_name_or_path}", + max_sequence_length=max_sequence_length, + ) From a3be634c571068ebb3b23578f665757f251dbb41 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 6 Mar 2026 13:46:38 -0800 Subject: [PATCH 06/89] Bump the npm group in /helm-frontend with 2 updates (#4097) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- helm-frontend/package.json | 4 ++-- helm-frontend/yarn.lock | 16 ++++++++-------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/helm-frontend/package.json b/helm-frontend/package.json index caca5c4fe51..bc706465939 100644 --- a/helm-frontend/package.json +++ b/helm-frontend/package.json @@ -31,7 +31,7 @@ "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^14.3.1", "@testing-library/user-event": "^14.6.1", - "@types/node": "^25.3.0", + "@types/node": "^25.3.3", "@types/react": "^18.2.15", "@types/react-dom": "^18.2.7", "@types/react-helmet": "^6.1.11", @@ -46,7 +46,7 @@ "eslint-plugin-react-refresh": "^0.4.26", "jest": "^29.6.1", "jsdom": "^22.1.0", - "postcss": "^8.4.27", + "postcss": "^8.5.8", "prettier": "^3.8.1", "tailwindcss": "^3.4.19", "typescript": "^5.9.3", diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index e57212683f0..a9f02436fdd 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -1508,10 +1508,10 @@ resolved "https://registry.yarnpkg.com/@types/ms/-/ms-0.7.32.tgz#f6cd08939ae3ad886fcc92ef7f0109dacddf61ab" integrity sha512-xPSg0jm4mqgEkNhowKgZFBNtwoEwF6gJ4Dhww+GFpm3IgtNseHQZ5IqdNwnquZEoANxyDAKDRAdVo4Z72VvD/g== -"@types/node@*", "@types/node@^25.3.0": - version "25.3.0" - resolved "https://registry.yarnpkg.com/@types/node/-/node-25.3.0.tgz#749b1bd4058e51b72e22bd41e9eab6ebd0180470" - integrity sha512-4K3bqJpXpqfg2XKGK9bpDTc6xO/xoUP/RBWS7AtRMug6zZFaRekiLzjVtAoZMquxoAbzBvy5nxQ7veS5eYzf8A== +"@types/node@*", "@types/node@^25.3.3": + version "25.3.3" + resolved "https://registry.yarnpkg.com/@types/node/-/node-25.3.3.tgz#605862544ee7ffd7a936bcbf0135a14012f1e549" + integrity sha512-DpzbrH7wIcBaJibpKo9nnSQL0MTRdnWttGyE5haGwK86xgMOkFLp7vEyfQPGLOJh5wNYiJ3V9PmUMDhV9u8kkQ== dependencies: undici-types "~7.18.0" @@ -4779,10 +4779,10 @@ postcss-value-parser@^4.0.0, postcss-value-parser@^4.2.0: resolved "https://registry.yarnpkg.com/postcss-value-parser/-/postcss-value-parser-4.2.0.tgz#723c09920836ba6d3e5af019f92bc0971c02e514" integrity sha512-1NNCs6uurfkVbeXG4S8JFT9t19m45ICnif8zWLd5oPSZ50QnwMfK+H3jv408d4jw/7Bttv5axS5IiHoLaVNHeQ== -postcss@^8, postcss@^8.4.27, postcss@^8.4.43, postcss@^8.4.47, postcss@^8.5.3: - version "8.5.6" - resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.5.6.tgz#2825006615a619b4f62a9e7426cc120b349a8f3c" - integrity sha512-3Ybi1tAuwAP9s0r1UQ2J4n5Y0G05bJkpUIO0/bI9MhwmD70S5aTWbXGBwxHrelT+XM1k6dM0pk+SwNkpTRN7Pg== +postcss@^8, postcss@^8.4.43, postcss@^8.4.47, postcss@^8.5.3, postcss@^8.5.8: + version "8.5.8" + resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.5.8.tgz#6230ecc8fb02e7a0f6982e53990937857e13f399" + integrity sha512-OW/rX8O/jXnm82Ey1k44pObPtdblfiuWnrd8X7GJ7emImCOstunGbXUpp7HdBrFQX6rJzn3sPT397Wp5aCwCHg== dependencies: nanoid "^3.3.11" picocolors "^1.1.1" From c5bf7149f0822bac1c07d7cf7773e77eb859d401 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 9 Mar 2026 10:01:09 -0700 Subject: [PATCH 07/89] Update Arabic content generation and finance scenarios (#4095) --- .../arabic_content_generation_annotator.py | 56 ++++- .../arabic_finance_calculation_annotator.py | 94 ++++++++ .../arabic_content_generation_metric.py | 43 ++++ .../arabic_finance_calculation_metric.py | 22 ++ .../run_entries_arabic_enterprise.conf | 12 + .../run_specs/arabic_enterprise_run_specs.py | 143 +++++++++++- .../arabic_content_generation_scenario.py | 72 ++++++ .../scenarios/arabic_finance_scenario.py | 125 ++++++++++ .../static/schema_arabic_enterprise.yaml | 220 ++++++++++++++++++ 9 files changed, 776 insertions(+), 11 deletions(-) create mode 100644 src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py create mode 100644 src/helm/benchmark/metrics/arabic_content_generation_metric.py create mode 100644 src/helm/benchmark/metrics/arabic_finance_calculation_metric.py create mode 100644 src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf create mode 100644 src/helm/benchmark/scenarios/arabic_content_generation_scenario.py create mode 100644 src/helm/benchmark/scenarios/arabic_finance_scenario.py create mode 100644 src/helm/benchmark/static/schema_arabic_enterprise.yaml diff --git a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py index c0a5dccf6e1..9df1287d3c2 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py @@ -29,13 +29,55 @@ _CRITERIA = { - "faithfulness": "Did the model say anything that isn’t supported by the facts?", - "completeness": "Did the model include the important facts?", - "style": "Does the model output follow the requested style, while keeping the facts intact?", - "tone_and_intent": "Does the output follow the requested tone and intent i.e. what the text is trying to do emotionally/strategically?", - "formality_and_register": "Does the output follow the requested formality and register i.e. how “corporate” vs “casual” the language is?", - "point_of_view_and_voice": "Does the output follow the requested point of view and voice i.e. who is “speaking” and how direct it is?", - "structure_and_formatting_habits": "Does the output follow the requested structure and formatting habits i.e. how the text is organized?", + "faithfulness": """Did the model say anything that isn’t supported by the facts in the user-provided content? + +1. The model frequently introduces multiple claims, details, or conclusions that are clearly not supported by or directly contradict the facts in the user-provided content. +2. The model includes several unsupported statements or minor factual additions that go beyond the user-provided content, though the core response still loosely aligns with the given information. +3. The model mostly relies on the user-provided content but adds at least one noticeable unsupported claim or speculative detail that is not grounded in the provided facts. +4. The model stays almost entirely within the bounds of the user-provided content, with only a very minor or ambiguous addition that is not explicitly supported but does not materially affect accuracy. +5. The model strictly adheres to the user-provided content and makes no claims, inferences, or details that are not directly supported by the given facts.""", + "completeness": """Did the model include all important facts from the user-provided content? + +1. The model omits most of the important facts from the user-provided content, including central ideas or key details necessary for understanding. +2. The model includes a few important facts but misses many key details or central points from the user-provided content. +3. The model includes several important facts from the user-provided content but omits at least one significant detail or key element. +4. The model includes nearly all important facts from the user-provided content, with only minor or non-essential details missing. +5. The model includes all important facts from the user-provided content with no meaningful omissions.""", + "style": """Does the model output follow the requested style, while keeping the facts intact? + +1. The output fails to follow the requested style and significantly distorts, omits, or introduces incorrect facts. +2. The output loosely reflects the requested style but contains noticeable stylistic inconsistencies and includes factual errors or misleading alterations. +3. The output generally follows the requested style with minor deviations and preserves most key facts, though small inaccuracies or slight distortions may appear. +4. The output closely adheres to the requested style and maintains factual accuracy, with only minimal stylistic or factual imperfections. +5. The output fully embodies the requested style while preserving all facts accurately, with no distortions, omissions, or inconsistencies.""", + "tone_and_intent": """Does the output follow the requested tone and intent i.e. what the text is trying to do emotionally/strategically? + +1. The output clearly fails to match the requested tone and intent, conveying an emotional or strategic approach that is inappropriate, contradictory, or entirely misaligned with what was asked. +2. The output shows minimal awareness of the requested tone and intent, with inconsistent or weak alignment that frequently undermines the desired emotional or strategic effect. +3. The output generally reflects the requested tone and intent but lacks consistency, nuance, or strength in fully delivering the intended emotional or strategic impact. +4. The output closely follows the requested tone and intent, maintaining consistent alignment and effectively supporting the intended emotional or strategic objective. +5. The output precisely and skillfully embodies the requested tone and intent throughout, enhancing the emotional or strategic purpose in a clear, cohesive, and compelling way.""", + "formality_and_register": """Does the output follow the requested formality and register i.e. how “corporate” vs “casual” the language is? + +1. The output completely ignores the requested formality and register, using language that is clearly inappropriate for the intended corporate or casual context. +2. The output shows minimal alignment with the requested formality and register, with frequent mismatches in tone, word choice, or level of professionalism. +3. The output partially follows the requested formality and register, but contains noticeable inconsistencies that weaken the overall tonal fit. +4. The output largely matches the requested formality and register, with only minor lapses in tone or phrasing that do not significantly affect appropriateness. +5. The output consistently and precisely matches the requested formality and register, demonstrating clear control over tone, word choice, and level of professionalism throughout.""", + "point_of_view_and_voice": """Does the output follow the requested point of view and voice i.e. who is “speaking” and how direct it is? + +1. The output completely ignores the requested formality and register, using a tone that is clearly inappropriate (e.g., highly casual when corporate was requested, or overly formal when casual was requested). +2. The output shows limited awareness of the requested formality and register, but the tone is frequently inconsistent or noticeably misaligned with expectations. +3. The output generally reflects the requested formality and register, though there are occasional lapses in tone, word choice, or phrasing that slightly weaken alignment. +4. The output closely follows the requested formality and register, with only minor inconsistencies that do not significantly affect overall tone. +5. The output fully and consistently matches the requested formality and register, demonstrating precise, intentional control of tone throughout.""", + "structure_and_formatting_habits": """Does the output follow the requested structure and formatting habits i.e. how the text is organized? + +1. The output ignores the requested structure and formatting habits entirely, showing no clear organization or adherence to the specified format. +2. The output attempts to follow the requested structure and formatting but does so inconsistently, with major organizational issues or missing required elements. +3. The output generally follows the requested structure and formatting, but includes noticeable inconsistencies, minor omissions, or organizational weaknesses. +4. The output closely follows the requested structure and formatting habits, with only minor deviations that do not significantly impact clarity or organization. +5. The output fully adheres to the requested structure and formatting habits, demonstrating clear, consistent, and well-organized presentation throughout.""", } diff --git a/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py b/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py new file mode 100644 index 00000000000..f3a2eaa94b4 --- /dev/null +++ b/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py @@ -0,0 +1,94 @@ +import re +from typing import Any + +from helm.benchmark.adaptation.request_state import RequestState +from helm.benchmark.annotation.annotator import Annotator +from helm.clients.auto_client import AutoClient +from helm.common.request import Request +from helm.proxy.retry import NonRetriableException + + +class AnnotatorResponseParseFailure(NonRetriableException): + def __init__(self, response_text: str, **kwargs): + self.response_text = response_text + super().__init__(kwargs) + + +class ArabicFinanceCalculationAnnotator(Annotator): + name = "arabic_finance_calculation" + + PROMPT_TEMPLATE = """Here is a math question, a reference answer, and a model output, delineated with start and end tags. Judge whether the model output is mathematically equivalent to the reference answer. + + +{{question}} + + + +{{reference_answer}} + + + +{{model_output}} + + +Respond with the following format: + + +INSERT_YOUR_REASONING_HERE + + +INSERT_YOUR_SCORE_HERE + + +Please output your one-sentence concise reasoning within the "reasoning" tags and your score within the "score" tags. Your reasoning should be less than 20 tokens. The score should be either 1 or 0. Do not output any other text. +""" # noqa: E501 + + def __init__(self, auto_client: AutoClient): + self._auto_client = auto_client + + def annotate(self, request_state: RequestState) -> Any: + assert request_state.result + assert len(request_state.result.completions) == 1 + question_text = request_state.instance.input.text + reference_text = request_state.instance.references[0].output.text + prediction_text = request_state.result.completions[0].text + + annotator_prompt = ( + self.PROMPT_TEMPLATE.replace("{{question}}", question_text) + .replace("{{reference_answer}}", reference_text) + .replace("{{model_output}}", prediction_text) + ) + annotator_request = Request( + model="openai/gpt-4o-2024-05-13", + model_deployment="openai/gpt-4o-2024-05-13", + prompt=annotator_prompt, + temperature=0.0, + max_tokens=100, + ) + annotator_response = self._auto_client.make_request(annotator_request) + if not annotator_response.success: + raise Exception(f"Annotation request failed: {annotator_response.error}") + assert len(annotator_response.completions) == 1 + annotator_response_text = annotator_response.completions[0].text + # fuzzy match regex check, allows for different casing, or forgetting / in end tag + reasoning_match = re.search( + r"<\s*reasoning\s*>(.*?)<\/?\s*reasoning\s*>", annotator_response_text, re.DOTALL | re.IGNORECASE + ) + score_match = re.search( + r"<\s*score\s*>(.*?)<\/?\s*score\s*>", annotator_response_text, re.DOTALL | re.IGNORECASE + ) + if not reasoning_match or not score_match: + raise AnnotatorResponseParseFailure( + message=f"Could not parse markup in raw response: '{annotator_response_text}'", + response_text=annotator_response_text, + ) + reasoning = reasoning_match.group(1).strip() + try: + score = float(score_match.group(1).strip()) + except ValueError: + raise AnnotatorResponseParseFailure( + message=f"Could not parse score as float from raw request: '{annotator_response_text}'", + response_text=annotator_response_text, + ) + + return {"reasoning": reasoning, "score": score} diff --git a/src/helm/benchmark/metrics/arabic_content_generation_metric.py b/src/helm/benchmark/metrics/arabic_content_generation_metric.py new file mode 100644 index 00000000000..009b7e3e769 --- /dev/null +++ b/src/helm/benchmark/metrics/arabic_content_generation_metric.py @@ -0,0 +1,43 @@ +from typing import Any, Dict, List + +from helm.benchmark.adaptation.adapter_spec import AdapterSpec +from helm.benchmark.adaptation.request_state import RequestState +from helm.benchmark.metrics.metric import Metric, MetricMetadata +from helm.benchmark.metrics.metric_name import MetricName +from helm.benchmark.metrics.metric_service import MetricService +from helm.benchmark.metrics.statistic import Stat + + +class ArabicContentGenerationMetric(Metric): + def evaluate_generation( + self, + adapter_spec: AdapterSpec, + request_state: RequestState, + metric_service: MetricService, + eval_cache_path: str, + ) -> List[Stat]: + scores: List[int] = [] + assert request_state.annotations + annotations: Dict[str, Any] = request_state.annotations["arabic_content_generation"] + for criteria_annotations in annotations.values(): + assert "score" in criteria_annotations + scores.append(criteria_annotations["score"]) + if not scores: + raise ValueError("ArabicContentGenerationMetric could not get scores from annotations.") + score = sum(scores) / len(scores) + score = (score - 1) / 4 + return [ + Stat(MetricName("arabic_content_generation_score")).add(score), + ] + + def get_metadata(self) -> List[MetricMetadata]: + return [ + MetricMetadata( + name="arabic_content_generation_score", + display_name="Arabic Content Generation Score", + short_display_name="Score", + description="LLM-judged quality score", + lower_is_better=False, + group="accuracy", + ), + ] diff --git a/src/helm/benchmark/metrics/arabic_finance_calculation_metric.py b/src/helm/benchmark/metrics/arabic_finance_calculation_metric.py new file mode 100644 index 00000000000..0e99e35a0db --- /dev/null +++ b/src/helm/benchmark/metrics/arabic_finance_calculation_metric.py @@ -0,0 +1,22 @@ +from typing import List + +from helm.benchmark.adaptation.adapter_spec import AdapterSpec +from helm.benchmark.adaptation.request_state import RequestState +from helm.benchmark.metrics.metric import Metric +from helm.benchmark.metrics.metric_name import MetricName +from helm.benchmark.metrics.metric_service import MetricService +from helm.benchmark.metrics.statistic import Stat + + +class ArabicFinanceCalculationMetric(Metric): + def evaluate_generation( + self, + adapter_spec: AdapterSpec, + request_state: RequestState, + metric_service: MetricService, + eval_cache_path: str, + ) -> List[Stat]: + assert request_state.annotations + assert "arabic_finance_calculation" in request_state.annotations + score = request_state.annotations["arabic_finance_calculation"]["score"] + return [Stat(MetricName("calculation_accuracy")).add(score)] diff --git a/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf b/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf new file mode 100644 index 00000000000..e1424189684 --- /dev/null +++ b/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf @@ -0,0 +1,12 @@ +# run entries for Arabic + +entries: [ + {description: "arabic_content_generation:category=all"}, + {description: "arabic_finance_bool"}, + {description: "arabic_finance_calculation"}, + {description: "arabic_finance_mcq"}, + + {description: "arabic_finance_bool:lang=en"}, + {description: "arabic_finance_calculation:lang=en"}, + {description: "arabic_finance_mcq:lang=en"}, +] diff --git a/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py b/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py index fae1f360591..0cd6cf01bb4 100644 --- a/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py +++ b/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py @@ -2,13 +2,22 @@ EXPERIMENTAL: Run specs here may have future reverse incompatible changes.""" -from helm.benchmark.adaptation.common_adapter_specs import get_generation_adapter_spec +from helm.benchmark.adaptation.adapter_spec import ADAPT_MULTIPLE_CHOICE_JOINT +from helm.benchmark.adaptation.common_adapter_specs import ( + get_generation_adapter_spec, + get_multiple_choice_adapter_spec, +) from helm.benchmark.annotation.annotator import AnnotatorSpec -from helm.benchmark.metrics.common_metric_specs import get_basic_metric_specs +from helm.benchmark.metrics.common_metric_specs import get_basic_metric_specs, get_exact_match_metric_specs +from helm.benchmark.metrics.metric import MetricSpec from helm.benchmark.run_spec import RunSpec, run_spec_function from helm.benchmark.scenarios.scenario import ScenarioSpec +_ARABIC_REFERENCE_PREFIX_CHARACTERS = ["أ", "ب", "ج"] +_ARABIC_OUTPUT_MAPPING_PATTERN = "(أ|ب|ج)" + + @run_spec_function("arabic_content_generation") def get_arabic_content_generation_spec(category: str, annotator_type: str = "absolute") -> RunSpec: """EXPERIMENTAL: This run spec here may have future reverse incompatible changes.""" @@ -24,7 +33,6 @@ def get_arabic_content_generation_spec(category: str, annotator_type: str = "abs stop_sequences=[], ) - # annotator_specs = [AnnotatorSpec(class_name="helm.benchmark.annotation.arabic_content_generation_annotator.ArabicContentGenerationAnnotator")] if annotator_type == "absolute": annotator_specs = [ AnnotatorSpec( @@ -45,7 +53,9 @@ def get_arabic_content_generation_spec(category: str, annotator_type: str = "abs ] else: raise ValueError(f"Unknown annotator_type {annotator_type}") - metric_specs = get_basic_metric_specs([]) + metric_specs = get_basic_metric_specs([]) + [ + MetricSpec(class_name="helm.benchmark.metrics.arabic_content_generation_metric.ArabicContentGenerationMetric") + ] return RunSpec( name=f"arabic_content_generation:category={category},annotator_type={annotator_type}", @@ -55,3 +65,128 @@ def get_arabic_content_generation_spec(category: str, annotator_type: str = "abs metric_specs=metric_specs, groups=["arabic_content_generation"], ) + + +@run_spec_function("arabic_finance_mcq") +def get_arabic_finance_mcq_spec(lang: str = "ar") -> RunSpec: + """EXPERIMENTAL: This run spec here may have future reverse incompatible changes.""" + + scenario_spec = ScenarioSpec( + class_name="helm.benchmark.scenarios.arabic_finance_scenario.ArabicFinanceMCQScenario", args={"lang": lang} + ) + + if lang == "en": + adapter_spec = get_multiple_choice_adapter_spec( + method=ADAPT_MULTIPLE_CHOICE_JOINT, + instructions="Answer the following multiple-choice question with only a single letter.", # noqa: E501 + input_noun="Question", + output_noun="Answer", + max_tokens=10, + ) + elif lang == "ar": + adapter_spec = get_multiple_choice_adapter_spec( + method=ADAPT_MULTIPLE_CHOICE_JOINT, + instructions="السؤال التالي هو سؤال متعدد الاختيارات. اختر الإجابة الصحيحة اكتب حرف الإجابة فقط، دون أي إضافات أخرى.", # noqa: E501 + input_noun="السؤال", + output_noun="الإجابة", + max_tokens=10, + reference_prefix_characters=_ARABIC_REFERENCE_PREFIX_CHARACTERS, + output_mapping_pattern=_ARABIC_OUTPUT_MAPPING_PATTERN, + ) + else: + raise ValueError(f"Unknown value for `lang`: {lang}") + + if lang == "ar": + run_spec_name = "arabic_finance_mcq" + else: + run_spec_name = f"arabic_finance_mcq:lang={lang}" + + return RunSpec( + name=run_spec_name, + scenario_spec=scenario_spec, + adapter_spec=adapter_spec, + metric_specs=get_exact_match_metric_specs(), + groups=["arabic_finance_mcq"], + ) + + +@run_spec_function("arabic_finance_bool") +def get_arabic_finance_bool_spec(lang: str = "ar") -> RunSpec: + """EXPERIMENTAL: This run spec here may have future reverse incompatible changes.""" + + scenario_spec = ScenarioSpec( + class_name="helm.benchmark.scenarios.arabic_finance_scenario.ArabicFinanceBoolScenario", args={"lang": lang} + ) + + if lang == "en": + instructions = 'Is the following passage true? Answer only with "Yes" or "No".' + elif lang == "ar": + instructions = 'هل المقطع التالي صحيح؟ أجب فقط بـ "نعم" أو "لا".' + else: + raise ValueError(f"Unknown value for `lang`: {lang}") + + adapter_spec = get_generation_adapter_spec( + instructions=instructions, + max_tokens=10, + stop_sequences=[], + ) + + if lang == "ar": + run_spec_name = "arabic_finance_bool" + else: + run_spec_name = f"arabic_finance_bool:lang={lang}" + + return RunSpec( + name=run_spec_name, + scenario_spec=scenario_spec, + adapter_spec=adapter_spec, + metric_specs=get_exact_match_metric_specs(), + groups=["arabic_finance_bool"], + ) + + +@run_spec_function("arabic_finance_calculation") +def get_arabic_finance_calculation_spec(lang: str = "ar") -> RunSpec: + """EXPERIMENTAL: This run spec here may have future reverse incompatible changes.""" + + scenario_spec = ScenarioSpec( + class_name="helm.benchmark.scenarios.arabic_finance_scenario.ArabicFinanceCalculationScenario", + args={"lang": lang}, + ) + + if lang == "en": + instructions = ( + "Answer the question, giving your reasoning beforehand. Wrap the final answer with the \\boxed{} command." + ) + elif lang == "ar": + instructions = "أجب عن السؤال، مع تقديم تعليلك مسبقًا. ضع الإجابة النهائية داخل الأمر \\boxed{}." + else: + raise ValueError(f"Unknown value for `lang`: {lang}") + + adapter_spec = get_generation_adapter_spec( + instructions=instructions, + max_tokens=2000, + stop_sequences=[], + ) + + annotator_specs = [ + AnnotatorSpec( + class_name="helm.benchmark.annotation.arabic_finance_calculation_annotator.ArabicFinanceCalculationAnnotator" + ) + ] + metric_specs = get_basic_metric_specs([]) + [ + MetricSpec(class_name="helm.benchmark.metrics.arabic_finance_calculation_metric.ArabicFinanceCalculationMetric") + ] + if lang == "ar": + run_spec_name = "arabic_finance_calculation" + else: + run_spec_name = f"arabic_finance_calculation:lang={lang}" + + return RunSpec( + name=run_spec_name, + scenario_spec=scenario_spec, + adapter_spec=adapter_spec, + metric_specs=metric_specs, + annotators=annotator_specs, + groups=["arabic_finance_calculation"], + ) diff --git a/src/helm/benchmark/scenarios/arabic_content_generation_scenario.py b/src/helm/benchmark/scenarios/arabic_content_generation_scenario.py new file mode 100644 index 00000000000..4e7f8463043 --- /dev/null +++ b/src/helm/benchmark/scenarios/arabic_content_generation_scenario.py @@ -0,0 +1,72 @@ +import os +from typing import List + +import datasets + +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.common.general import ensure_directory_exists +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Reference, + TEST_SPLIT, + CORRECT_TAG, + Input, + Output, + ScenarioMetadata, +) + + +class ArabicContentGenerationScenario(Scenario): + name = "arabic_content_generation" + description = "Arabic Content Generation" + tags = ["content generation"] + + def __init__(self, category: str): + super().__init__() + self.category = category + + def get_instances(self, output_path: str) -> List[Instance]: + cache_dir = os.path.join(output_path, "data") + ensure_directory_exists(cache_dir) + dataset = datasets.load_dataset( + "yifanmai/arabic-enterprise", + "content_generation", + cache_dir=cache_dir, + split="test", + # TODO: Set revision + ) + assert isinstance(dataset, datasets.Dataset) + + instances: List[Instance] = [] + for row in dataset: + if self.category != "all" and self.category != row["category"]: + continue + prompt = f"## Facts\n\n{row['facts']}\n\n## Style\n\n{row['style']}" + instance = Instance( + id=f"id{row['id']}", + input=Input(text=prompt), + references=[ + Reference(Output(text=row["text"].strip()), tags=[CORRECT_TAG]), + ], + split=TEST_SPLIT, + ) + instances.append(instance) + + return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name=self.name, + display_name="Arabic Content Generation", + description=self.description, + main_metric="exact_match", + main_split="test", + taxonomy=TaxonomyInfo( + task="content generation", + what="news articles", + who="journalists", + when="before 2026", + language="Arabic", + ), + ) diff --git a/src/helm/benchmark/scenarios/arabic_finance_scenario.py b/src/helm/benchmark/scenarios/arabic_finance_scenario.py new file mode 100644 index 00000000000..842c57dd83f --- /dev/null +++ b/src/helm/benchmark/scenarios/arabic_finance_scenario.py @@ -0,0 +1,125 @@ +import os +from typing import Dict, List + +import datasets + +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Reference, + TEST_SPLIT, + CORRECT_TAG, + Input, + Output, +) +from helm.common.general import ensure_directory_exists + + +class ArabicFinanceScenario(Scenario): + + name = "arabic_finance" + description = "Arabic Finance" + tags = ["finance"] + + def __init__(self, task: str, lang: str): + super().__init__() + self.task = task + self.lang = lang + if self.lang == "en": + self.question_key = "question" + self.choice_key = "choice" + self.ground_truth_key = "ground_truth" + elif self.lang == "ar": + self.question_key = "question_ar" + self.choice_key = "choice_ar" + self.ground_truth_key = "ground_truth_ar" + else: + raise Exception(f"Unsupported language {lang}") + + def get_references(self, row: Dict[str, str]) -> List[Reference]: + raise NotImplementedError() + + def get_instances(self, output_path: str) -> List[Instance]: + cache_dir = os.path.join(output_path, "data") + ensure_directory_exists(cache_dir) + dataset = datasets.load_dataset( + "yifanmai/arabic-enterprise", + "finance", + cache_dir=cache_dir, + split="test", + # TODO: Set revision + ) + assert isinstance(dataset, datasets.Dataset) + + instances: List[Instance] = [] + for row in dataset: + if row["task"] != self.task: + continue + input = Input(text=row[self.question_key]) + references = self.get_references(row) + instances.append( + Instance( + id=row["id"], + input=input, + references=references, + split=TEST_SPLIT, + ) + ) + + return instances + + +class ArabicFinanceBoolScenario(ArabicFinanceScenario): + name = "arabic_finance_bool" + description = "Arabic Finance Bool" + tags = ["finance"] + + def __init__(self, lang: str): + super().__init__(task="bool", lang=lang) + + def get_references(self, row: Dict[str, str]) -> List[Reference]: + if float(row[self.ground_truth_key]) == 1.0: + return [Reference(output=Output(text="نعم" if self.lang == "ar" else "Yes"), tags=[CORRECT_TAG])] + elif float(row[self.ground_truth_key]) == 0.0: + return [Reference(output=Output(text="لا" if self.lang == "ar" else "No"), tags=[CORRECT_TAG])] + else: + raise ValueError(f"Unknown ground truth value {row[self.ground_truth_key]}") + + +class ArabicFinanceMCQScenario(ArabicFinanceScenario): + name = "arabic_finance_mcq" + description = "Arabic Finance MCQ" + tags = ["finance"] + + def __init__(self, lang: str): + super().__init__(task="mcq", lang=lang) + + def get_references(self, row: Dict[str, str]) -> List[Reference]: + correct_choice_letter = row[self.ground_truth_key] + raw_choices = row[self.choice_key] + references: List[Reference] = [] + for choice_index, raw_choice in enumerate(raw_choices.split("\n")): + choice_letter = chr(choice_index + ord("A")) + choice_prefix = f"{choice_letter}. " + assert raw_choice.startswith(choice_prefix) + choice_text = raw_choice.removeprefix(choice_prefix) + references.append( + Reference( + output=Output(text=choice_text), + tags=[CORRECT_TAG] if choice_letter == correct_choice_letter else [], + ) + ) + assert len(references) == 3 + return references + + +class ArabicFinanceCalculationScenario(ArabicFinanceScenario): + name = "arabic_finance_calculation" + description = "Arabic Finance Calculation" + tags = ["finance"] + + def __init__(self, lang: str): + super().__init__(task="calcu", lang=lang) + + def get_references(self, row: Dict[str, str]) -> List[Reference]: + return [Reference(output=Output(text=row[self.ground_truth_key]), tags=[CORRECT_TAG])] diff --git a/src/helm/benchmark/static/schema_arabic_enterprise.yaml b/src/helm/benchmark/static/schema_arabic_enterprise.yaml new file mode 100644 index 00000000000..ff07df8037d --- /dev/null +++ b/src/helm/benchmark/static/schema_arabic_enterprise.yaml @@ -0,0 +1,220 @@ +--- +# Schema for Arabic scenarios +############################################################ +metrics: + # Infrastructure metrics: + - name: num_perplexity_tokens + display_name: '# tokens' + description: Average number of tokens in the predicted output (for language modeling, the input too). + - name: num_bytes + display_name: '# bytes' + description: Average number of bytes in the predicted output (for language modeling, the input too). + + - name: num_references + display_name: '# ref' + description: Number of references. + - name: num_train_trials + display_name: '# trials' + description: Number of trials, where in each trial we choose an independent, random set of training instances. + - name: estimated_num_tokens_cost + display_name: 'cost' + description: An estimate of the number of tokens (including prompt and output completions) needed to perform the request. + - name: num_prompt_tokens + display_name: '# prompt tokens' + description: Number of tokens in the prompt. + - name: num_prompt_characters + display_name: '# prompt chars' + description: Number of characters in the prompt. + - name: num_completion_tokens + display_name: '# completion tokens' + description: Actual number of completion tokens (over all completions). + - name: num_output_tokens + display_name: '# output tokens' + description: Actual number of output tokens. + - name: max_num_output_tokens + display_name: 'Max output tokens' + description: Maximum number of output tokens (overestimate since we might stop earlier due to stop sequences). + - name: num_requests + display_name: '# requests' + description: Number of distinct API requests. + - name: num_instances + display_name: '# eval' + description: Number of evaluation instances. + - name: num_train_instances + display_name: '# train' + description: Number of training instances (e.g., in-context examples). + - name: prompt_truncated + display_name: truncated + description: Fraction of instances where the prompt itself was truncated (implies that there were no in-context examples). + - name: finish_reason_length + display_name: finish b/c length + description: Fraction of instances where the the output was terminated because of the max tokens limit. + - name: finish_reason_stop + display_name: finish b/c stop + description: Fraction of instances where the the output was terminated because of the stop sequences. + - name: finish_reason_endoftext + display_name: finish b/c endoftext + description: Fraction of instances where the the output was terminated because the end of text token was generated. + - name: finish_reason_unknown + display_name: finish b/c unknown + description: Fraction of instances where the the output was terminated for unknown reasons. + - name: num_completions + display_name: '# completions' + description: Number of completions. + - name: predicted_index + display_name: Predicted index + description: Integer index of the reference (0, 1, ...) that was predicted by the model (for multiple-choice). + - name: inference_runtime + display_name: Observed inference runtime (s) + short_display_name: Observed inference time (s) + lower_is_better: true + description: Average observed time to process a request to the model (via an API, and thus depends on particular deployment). + + # Accuracy metrics: + - name: exact_match + display_name: Exact match + short_display_name: EM + description: Fraction of instances that the predicted output matches a correct reference exactly. + lower_is_better: false + - name: quasi_exact_match + display_name: Quasi-exact match + short_display_name: EM + description: Fraction of instances that the predicted output matches a correct reference up to light processing. + lower_is_better: false + - name: prefix_exact_match + display_name: Prefix exact match + short_display_name: PEM + description: Fraction of instances that the predicted output matches the prefix of a correct reference exactly. + lower_is_better: false + - name: quasi_prefix_exact_match + # TODO: should call this prefix_quasi_exact_match + display_name: Prefix quasi-exact match + short_display_name: PEM + description: Fraction of instances that the predicted output matches the prefix of a correct reference up to light processing. + lower_is_better: false + - name: calculation_accuracy + display_name: Calculation accuracy + short_display_name: Accuracy + description: Fraction of instances that had final answer that was mathematically equivalent to the reference answer. + lower_is_better: false + - name: arabic_content_generation_score + display_name: Model Judged Score + short_display_name: Score + description: Model-judged score. + lower_is_better: false + +############################################################ +perturbations: [] + +############################################################ +metric_groups: + - name: accuracy + display_name: Accuracy + aggregation_strategies: + - mean + metrics: + - name: ${main_name} + split: ${main_split} + + - name: efficiency + display_name: Efficiency + aggregation_strategies: + - mean + metrics: + - name: inference_runtime + split: ${main_split} + + - name: general_information + display_name: General information + hide_win_rates: true + metrics: + - name: num_instances + split: ${main_split} + - name: num_train_instances + split: ${main_split} + - name: prompt_truncated + split: ${main_split} + - name: num_prompt_tokens + split: ${main_split} + - name: num_output_tokens + split: ${main_split} + +############################################################ +run_groups: + - name: arabic_scenarios + display_name: Arabic Scenarios + description: Arabic Scenarios + category: Scenarios + subgroups: + - arabic_content_generation + - arabic_finance_calculation + - arabic_finance_mcq + - arabic_finance_bool + + - name: arabic_content_generation + display_name: Content Generation + description: Content Generation + metric_groups: + - accuracy + - efficiency + - general_information + environment: + main_name: arabic_content_generation_score + main_split: test + taxonomy: + task: "?" + what: "?" + who: "?" + when: "2025" + language: Arabic + + - name: arabic_finance_calculation + display_name: Finance Calculation + description: Finance Calculation + metric_groups: + - accuracy + - efficiency + - general_information + environment: + main_name: calculation_accuracy + main_split: test + taxonomy: + task: "?" + what: "?" + who: "?" + when: "2025" + language: English + + - name: arabic_finance_mcq + display_name: Finance MCQ + description: Finance MCQ + metric_groups: + - accuracy + - efficiency + - general_information + environment: + main_name: exact_match + main_split: test + taxonomy: + task: "?" + what: "?" + who: "?" + when: "2025" + language: English + + - name: arabic_finance_bool + display_name: Finance Bool + description: Finance Bool + metric_groups: + - accuracy + - efficiency + - general_information + environment: + main_name: quasi_exact_match + main_split: test + taxonomy: + task: "?" + what: "?" + who: "?" + when: "2025" + language: English From 9de0cbbaae92e2f6daefdc68b41d6104e097268f Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 9 Mar 2026 14:53:15 -0700 Subject: [PATCH 08/89] Temporary experimental changes to Arabic Enterprise (#4102) --- .../arabic_content_generation_annotator.py | 51 ++++--------------- .../arabic_finance_calculation_annotator.py | 4 +- .../run_entries_arabic_enterprise.conf | 4 -- 3 files changed, 12 insertions(+), 47 deletions(-) diff --git a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py index 9df1287d3c2..2ce080eca99 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py @@ -43,41 +43,13 @@ 3. The model includes several important facts from the user-provided content but omits at least one significant detail or key element. 4. The model includes nearly all important facts from the user-provided content, with only minor or non-essential details missing. 5. The model includes all important facts from the user-provided content with no meaningful omissions.""", - "style": """Does the model output follow the requested style, while keeping the facts intact? - -1. The output fails to follow the requested style and significantly distorts, omits, or introduces incorrect facts. -2. The output loosely reflects the requested style but contains noticeable stylistic inconsistencies and includes factual errors or misleading alterations. -3. The output generally follows the requested style with minor deviations and preserves most key facts, though small inaccuracies or slight distortions may appear. -4. The output closely adheres to the requested style and maintains factual accuracy, with only minimal stylistic or factual imperfections. -5. The output fully embodies the requested style while preserving all facts accurately, with no distortions, omissions, or inconsistencies.""", - "tone_and_intent": """Does the output follow the requested tone and intent i.e. what the text is trying to do emotionally/strategically? - -1. The output clearly fails to match the requested tone and intent, conveying an emotional or strategic approach that is inappropriate, contradictory, or entirely misaligned with what was asked. -2. The output shows minimal awareness of the requested tone and intent, with inconsistent or weak alignment that frequently undermines the desired emotional or strategic effect. -3. The output generally reflects the requested tone and intent but lacks consistency, nuance, or strength in fully delivering the intended emotional or strategic impact. -4. The output closely follows the requested tone and intent, maintaining consistent alignment and effectively supporting the intended emotional or strategic objective. -5. The output precisely and skillfully embodies the requested tone and intent throughout, enhancing the emotional or strategic purpose in a clear, cohesive, and compelling way.""", - "formality_and_register": """Does the output follow the requested formality and register i.e. how “corporate” vs “casual” the language is? - -1. The output completely ignores the requested formality and register, using language that is clearly inappropriate for the intended corporate or casual context. -2. The output shows minimal alignment with the requested formality and register, with frequent mismatches in tone, word choice, or level of professionalism. -3. The output partially follows the requested formality and register, but contains noticeable inconsistencies that weaken the overall tonal fit. -4. The output largely matches the requested formality and register, with only minor lapses in tone or phrasing that do not significantly affect appropriateness. -5. The output consistently and precisely matches the requested formality and register, demonstrating clear control over tone, word choice, and level of professionalism throughout.""", - "point_of_view_and_voice": """Does the output follow the requested point of view and voice i.e. who is “speaking” and how direct it is? - -1. The output completely ignores the requested formality and register, using a tone that is clearly inappropriate (e.g., highly casual when corporate was requested, or overly formal when casual was requested). -2. The output shows limited awareness of the requested formality and register, but the tone is frequently inconsistent or noticeably misaligned with expectations. -3. The output generally reflects the requested formality and register, though there are occasional lapses in tone, word choice, or phrasing that slightly weaken alignment. -4. The output closely follows the requested formality and register, with only minor inconsistencies that do not significantly affect overall tone. -5. The output fully and consistently matches the requested formality and register, demonstrating precise, intentional control of tone throughout.""", - "structure_and_formatting_habits": """Does the output follow the requested structure and formatting habits i.e. how the text is organized? - -1. The output ignores the requested structure and formatting habits entirely, showing no clear organization or adherence to the specified format. -2. The output attempts to follow the requested structure and formatting but does so inconsistently, with major organizational issues or missing required elements. -3. The output generally follows the requested structure and formatting, but includes noticeable inconsistencies, minor omissions, or organizational weaknesses. -4. The output closely follows the requested structure and formatting habits, with only minor deviations that do not significantly impact clarity or organization. -5. The output fully adheres to the requested structure and formatting habits, demonstrating clear, consistent, and well-organized presentation throughout.""", + "style": """"Did the model output follow the requested style, including the specified tone, intent, level of formality, register, point of view, and voice? + +1. The output clearly ignores or contradicts the requested style, failing to match the specified tone, intent, formality, register, point of view, or voice. +2. The output shows minimal alignment with the requested style, with major inconsistencies or frequent deviations from the specified tone, formality, point of view, or voice. +3. The output generally reflects the requested style but includes noticeable inconsistencies or partial mismatches in tone, formality, register, point of view, or voice. +4. The output closely follows the requested style with only minor or occasional deviations in tone, intent, formality, register, point of view, or voice. +5. The output fully and consistently matches the requested style, accurately maintaining the specified tone, intent, level of formality, register, point of view, and voice throughout.""", } @@ -105,8 +77,8 @@ def annotate(self, request_state: RequestState) -> Any: ) annotator_request = Request( - model="openai/gpt-5.1-2025-11-13", - model_deployment="openai/gpt-5.1-2025-11-13", + model="openai/gpt-4.1-nano-2025-04-14", + model_deployment="openai/gpt-4.1-nano-2025-04-14", prompt=annotator_prompt, temperature=0.0, max_tokens=2000, @@ -115,10 +87,7 @@ def annotate(self, request_state: RequestState) -> Any: assert len(annotator_response.completions) == 1 annotator_response_text = annotator_response.completions[0].text - annotations[criterion_id] = { - "prompt": annotator_prompt, - **self._parse_annotator_response(annotator_response_text), - } + annotations[criterion_id] = self._parse_annotator_response(annotator_response_text) return annotations def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, Union[int, str]]: diff --git a/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py b/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py index f3a2eaa94b4..45160bc8b4e 100644 --- a/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py @@ -59,8 +59,8 @@ def annotate(self, request_state: RequestState) -> Any: .replace("{{model_output}}", prediction_text) ) annotator_request = Request( - model="openai/gpt-4o-2024-05-13", - model_deployment="openai/gpt-4o-2024-05-13", + model="openai/gpt-4.1-nano-2025-04-14", + model_deployment="openai/gpt-4.1-nano-2025-04-14", prompt=annotator_prompt, temperature=0.0, max_tokens=100, diff --git a/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf b/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf index e1424189684..18549b4d577 100644 --- a/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf +++ b/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf @@ -5,8 +5,4 @@ entries: [ {description: "arabic_finance_bool"}, {description: "arabic_finance_calculation"}, {description: "arabic_finance_mcq"}, - - {description: "arabic_finance_bool:lang=en"}, - {description: "arabic_finance_calculation:lang=en"}, - {description: "arabic_finance_mcq:lang=en"}, ] From 1e56c9455dd50ae2f6e4e34cd070f7c82b66ae4d Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 10 Mar 2026 17:08:48 -0700 Subject: [PATCH 09/89] Remove the human-evaluation optional dependency (#4101) --- constraints.txt | 10 - pyproject.toml | 15 +- requirements.txt | 2 +- .../proxy/critique/scale_critique_client.py | 6 +- .../critique/surge_ai_critique_client.py | 6 +- uv.lock | 757 ++++++++---------- 6 files changed, 332 insertions(+), 464 deletions(-) delete mode 100644 constraints.txt diff --git a/constraints.txt b/constraints.txt deleted file mode 100644 index 68fd6e4ce98..00000000000 --- a/constraints.txt +++ /dev/null @@ -1,10 +0,0 @@ -# Dependency version constraints for `uv pip compile` -# See: https://docs.astral.sh/uv/pip/compile/#adding-constraints - -# Constrain PyTorch versions to the same versions in install-dev.sh -torch==2.5.1 -torchvision==0.20.1 - -# Security upgrades -protobuf>=6.33.5 -urllib3>=2.6.3 diff --git a/pyproject.toml b/pyproject.toml index 5ee0282e226..c01cc7be109 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,11 +62,6 @@ proxy-server = [ "gunicorn>=20.1", ] -human-evaluation = [ - "scaleapi~=2.13", - "surge-api~=1.1", -] - dspy = [ "dspy~=3.0", ] @@ -298,7 +293,7 @@ heim = [ "flax~=0.8.5; python_version < '3.10'", "ftfy~=6.1", "Unidecode~=1.3", - "wandb~=0.16", + "wandb~=0.25", # HEIM perturbations "google-cloud-translate~=3.11", @@ -397,7 +392,6 @@ all = [ "crfm-helm[vlm]", "crfm-helm[codeinsights]", "crfm-helm[lmkt]", - # crfm-helm[human-evaluation] is excluded because it conflicts with crfm-helm[ibm] # crfm-helm[audiolm] is excluded because it requires transformers~=4.48 # crfm-helm[seahelm] is excluded because pyonmttok does not support Python 3.12 # crfm-helm[dev] is excluded because end-users don't need it. @@ -463,13 +457,6 @@ override-dependencies = [ "numpy>=1.26,<3", ] -conflicts = [ - [ - { extra = "ibm" }, - { extra = "human-evaluation" }, - ], -] - [tool.setuptools] package-dir = { "" = "src" } zip-safe = false diff --git a/requirements.txt b/requirements.txt index ae9d4a70793..731da71d0c3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,7 @@ colorcet==3.1.0 colorlog==6.10.1 confection==0.1.5 contourpy==1.3.2 -cryptography==46.0.5 +cryptography==44.0.1 cycler==0.12.1 cymem==2.0.13 dacite==1.9.2 diff --git a/src/helm/proxy/critique/scale_critique_client.py b/src/helm/proxy/critique/scale_critique_client.py index 0c6808f5e94..d486721c2c6 100644 --- a/src/helm/proxy/critique/scale_critique_client.py +++ b/src/helm/proxy/critique/scale_critique_client.py @@ -14,7 +14,7 @@ CritiqueTaskTemplate, CritiqueResponse, ) -from helm.common.optional_dependencies import handle_module_not_found_error +from helm.common.optional_dependencies import OptionalDependencyNotInstalled from helm.proxy.critique.critique_client import CritiqueClient try: @@ -22,7 +22,9 @@ from scaleapi.tasks import TaskType, TaskStatus from scaleapi.exceptions import ScaleDuplicateResource except ModuleNotFoundError as e: - handle_module_not_found_error(e, ["human-evaluation"]) + raise OptionalDependencyNotInstalled( + "`scaleapi` is not installed. Please install the `scaleapi` package manually." + ) from e class ScaleCritiqueClientError(Exception): diff --git a/src/helm/proxy/critique/surge_ai_critique_client.py b/src/helm/proxy/critique/surge_ai_critique_client.py index 6791f5b1583..b51aa1f419d 100644 --- a/src/helm/proxy/critique/surge_ai_critique_client.py +++ b/src/helm/proxy/critique/surge_ai_critique_client.py @@ -11,14 +11,16 @@ CritiqueTaskTemplate, ) from helm.common.hierarchical_logger import hlog -from helm.common.optional_dependencies import handle_module_not_found_error +from helm.common.optional_dependencies import OptionalDependencyNotInstalled from helm.proxy.critique.critique_client import CritiqueClient try: import surge from surge import questions as surge_questions except ModuleNotFoundError as e: - handle_module_not_found_error(e, ["human-evaluation"]) + raise OptionalDependencyNotInstalled( + "`surge-api` is not installed. Please install the `surge-api` package manually." + ) from e _surge_cache_lock = threading.Lock() diff --git a/uv.lock b/uv.lock index 4a0b902ca16..05bbd1a0b91 100644 --- a/uv.lock +++ b/uv.lock @@ -13,10 +13,6 @@ resolution-markers = [ "python_full_version < '3.11' and sys_platform == 'linux'", "python_full_version < '3.11' and sys_platform != 'linux'", ] -conflicts = [[ - { package = "crfm-helm", extra = "human-evaluation" }, - { package = "crfm-helm", extra = "ibm" }, -]] [manifest] overrides = [ @@ -39,8 +35,8 @@ version = "0.34.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "packaging" }, { name = "psutil" }, { name = "pyyaml" }, @@ -65,13 +61,13 @@ dependencies = [ { name = "google-cloud-storage" }, { name = "logzio-python-handler" }, { name = "msgspec" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "omegaconf" }, { name = "packaging" }, { name = "rich" }, - { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "smashed", extra = ["remote"] }, { name = "tokenizers" }, { name = "torch" }, @@ -121,7 +117,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - { name = "async-timeout", marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "async-timeout", marker = "python_full_version < '3.11'" }, { name = "attrs" }, { name = "frozenlist" }, { name = "multidict" }, @@ -252,7 +248,7 @@ version = "1.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "frozenlist" }, - { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" } wheels = [ @@ -266,7 +262,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mako" }, { name = "sqlalchemy" }, - { name = "tomli", marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "tomli", marker = "python_full_version < '3.11'" }, { name = "typing-extensions" }, ] sdist = { url = "https://files.pythonhosted.org/packages/49/cc/aca263693b2ece99fa99a09b6d092acb89973eb2bb575faef1777e04f8b4/alembic-1.18.1.tar.gz", hash = "sha256:83ac6b81359596816fb3b893099841a0862f2117b2963258e965d70dc62fb866", size = 2044319, upload-time = "2026-01-14T18:53:14.907Z" } @@ -332,9 +328,9 @@ name = "anyio" version = "4.12.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "exceptiongroup", marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "exceptiongroup", marker = "python_full_version < '3.11'" }, { name = "idna" }, - { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" } wheels = [ @@ -445,8 +441,8 @@ name = "audioread" version = "3.1.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "standard-aifc", marker = "python_full_version >= '3.13' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "standard-sunau", marker = "python_full_version >= '3.13' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "standard-aifc", marker = "python_full_version >= '3.13'" }, + { name = "standard-sunau", marker = "python_full_version >= '3.13'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a1/4a/874ecf9b472f998130c2b5e145dcdb9f6131e84786111489103b66772143/audioread-3.1.0.tar.gz", hash = "sha256:1c4ab2f2972764c896a8ac61ac53e261c8d29f0c6ccd652f84e18f08a4cab190", size = 20082, upload-time = "2025-10-26T19:44:13.484Z" } wheels = [ @@ -560,8 +556,8 @@ version = "0.3.13" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "matplotlib" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "packaging" }, { name = "pandas" }, { name = "requests" }, @@ -584,8 +580,8 @@ dependencies = [ { name = "packaging" }, { name = "pathspec" }, { name = "platformdirs" }, - { name = "tomli", marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "typing-extensions", marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "tomli", marker = "python_full_version < '3.11'" }, + { name = "typing-extensions", marker = "python_full_version < '3.11'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/8f/5f/bac24a952668c7482cfdb4ebf91ba57a796c9da8829363a772040c1a3312/black-24.3.0.tar.gz", hash = "sha256:a0c9c4a0771afc6919578cec71ce82a3e31e054904e7197deacbc9382671c41f", size = 634292, upload-time = "2024-03-15T19:35:43.699Z" } wheels = [ @@ -610,10 +606,10 @@ version = "0.3.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "nltk" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, 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"https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "spacy" }, { name = "sqlitedict" }, { name = "torch" }, @@ -1379,8 +1374,8 @@ all = [ { name = "multilingual-clip" }, { name = "nudenet" }, { name = "numba" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "omegaconf" }, { name = "open-clip-torch" }, { name = "openai" }, @@ -1398,10 +1393,10 @@ all = [ { name = "qwen-vl-utils" }, { name = "reka-api" }, { name = "sacrebleu" }, - { name = "scikit-image", version = "0.25.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scikit-image", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "scikit-image", version = "0.25.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scikit-image", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "seaborn" }, { name = "selenium" }, { name = "sentence-transformers" }, @@ -1451,8 +1446,8 @@ audiolm = [ { name = "pydantic" }, { name = "pydub" }, { name = "rapidfuzz" }, - { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "soundfile" }, { name = "tiktoken" }, { name = "torchvision" }, @@ -1517,8 +1512,8 @@ heim = [ { name = "lpips" }, { name = "multilingual-clip" }, { name = "nudenet" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "omegaconf" }, { name = "openai" }, { name = "opencv-python" }, @@ -1526,8 +1521,8 @@ heim = [ { name = "pydantic" }, { name = "pytorch-fid" }, { name = "pytorch-lightning" }, - { name = "scikit-image", version = "0.25.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scikit-image", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "scikit-image", version = "0.25.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scikit-image", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "tensorflow" }, { name = "tiktoken" }, { name = "timm" }, @@ -1536,13 +1531,9 @@ heim = [ { name = "unidecode" }, { name = "wandb" }, ] -human-evaluation = [ - { name = "scaleapi" }, - { name = "surge-api" }, -] ibm = [ - { name = "ibm-watsonx-ai", version = "1.3.42", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and extra == 'extra-9-crfm-helm-ibm') or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "ibm-watsonx-ai", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and extra == 'extra-9-crfm-helm-ibm') or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "ibm-watsonx-ai", version = "1.3.42", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "ibm-watsonx-ai", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, ] ibm-enterprise-scenarios = [ { name = "openpyxl" }, @@ -1678,8 +1669,8 @@ vlm = [ { name = "pydantic" }, { name = "qwen-vl-utils" }, { name = "reka-api" }, - { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "selenium" }, { name = "tiktoken" }, { name = "torch" }, @@ -1872,7 +1863,6 @@ requires-dist = [ { name = "rouge-score", specifier = "~=0.1.2" }, { name = "sacrebleu", marker = "extra == 'metrics'", specifier = "~=2.2" }, { name = "sacrebleu", marker = "extra == 'seahelm'", specifier = "~=2.2" }, - { name = "scaleapi", marker = "extra == 'human-evaluation'", specifier = "~=2.13" }, { name = "scikit-image", marker = "extra == 'heim'", specifier = "==0.*,>=0.22" }, { name = "scikit-learn", specifier = ">=1.1" }, { name = "scipy", specifier = ">=1.10" }, @@ -1888,7 +1878,6 @@ requires-dist = [ { name = "spacy", specifier = "~=3.5" }, { name = "sqlitedict", specifier = ">=2.1.0,<3.0" }, { name = "summ-eval", marker = "extra == 'summarization'", specifier = "~=0.892" }, - { name = "surge-api", marker = "extra == 'human-evaluation'", specifier = "~=1.1" }, { name = "tensorflow", marker = "extra == 'heim'", specifier = "~=2.11" }, { name = "tiktoken", marker = "extra == 'openai'", specifier = "~=0.7" }, { name = "timm", marker = "extra == 'heim'", specifier = "~=0.6.12" }, @@ -1909,13 +1898,13 @@ requires-dist = [ { name = "uncertainty-calibration", specifier = "~=0.1.4" }, { name = "unidecode", marker = "extra == 'cleva'", specifier = "~=1.3" }, { name = "unidecode", marker = "extra == 'heim'", specifier = "~=1.3" }, - { name = "wandb", marker = "extra == 'heim'", specifier = "~=0.16" }, + { name = "wandb", marker = "extra == 'heim'", specifier = "~=0.25" }, { name = "websocket-client", marker = "extra == 'anthropic'", specifier = "~=1.3" }, { name = "writerai", marker = "extra == 'writer'", specifier = "~=4.0" }, { name = "xlrd", marker = "extra == 'scenarios'", specifier = "~=2.0" }, { name = "zstandard", specifier = "~=0.18.0" }, ] -provides-extras = ["proxy-server", "human-evaluation", "dspy", "scenarios", "metrics", "ranking", "summarization", "plots", "decodingtrust", "slurm", "cleva", "images", "mongo", "unitxt", "seahelm", "accelerate", "aleph-alpha", "allenai", "amazon", "anthropic", "cohere", "writer", "mistral", "openai", "google", "together", "yandex", "models", "reka", "vlm", "ibm-enterprise-scenarios", "ibm", "image2struct", "heim", "medhelm", "audiolm", "codeinsights", "lmkt", "all", "ci", "litellm"] +provides-extras = ["proxy-server", "dspy", "scenarios", "metrics", "ranking", "summarization", "plots", "decodingtrust", "slurm", "cleva", "images", "mongo", "unitxt", "seahelm", "accelerate", "aleph-alpha", "allenai", "amazon", "anthropic", "cohere", "writer", "mistral", "openai", "google", "together", "yandex", "models", "reka", "vlm", "ibm-enterprise-scenarios", "ibm", "image2struct", "heim", "medhelm", "audiolm", "codeinsights", "lmkt", "all", "ci", "litellm"] [package.metadata.requires-dev] dev = [ @@ -1941,7 +1930,7 @@ name = "cryptography" version = "44.0.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "cffi", marker = "platform_python_implementation != 'PyPy' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "cffi", marker = "platform_python_implementation != 'PyPy'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/c7/67/545c79fe50f7af51dbad56d16b23fe33f63ee6a5d956b3cb68ea110cbe64/cryptography-44.0.1.tar.gz", hash = "sha256:f51f5705ab27898afda1aaa430f34ad90dc117421057782022edf0600bec5f14", size = 710819, upload-time = "2025-02-11T15:50:58.39Z" } wheels = [ @@ -2127,8 +2116,8 @@ dependencies = [ { name = "fsspec", extra = ["http"] }, { name = "huggingface-hub" }, { name = "multiprocess" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = 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"https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pillow" }, { name = "regex" }, { name = "requests" }, @@ -2250,8 +2239,8 @@ dependencies = [ { name = "json-repair" }, { name = "litellm" }, { name = "magicattr" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "openai" }, { name = "optuna" }, { name = "orjson" }, @@ -2355,8 +2344,8 @@ dependencies = [ { name = "fsspec", extra = ["http"] }, { name = "huggingface-hub" }, { name = "multiprocess" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "packaging" }, { name = "pandas" }, { name = "requests" }, @@ -2373,7 +2362,7 @@ name = "exceptiongroup" version = "1.3.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "typing-extensions", marker = "python_full_version < '3.11'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" } wheels = [ @@ -2397,13 +2386,13 @@ name = "fairlearn" version = "0.9.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pandas" }, - { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "scipy", version = "1.15.3", source = { registry = 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= "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "optax" }, { name = "orbax-checkpoint" }, { name = "pyyaml" }, { name = "rich" }, - { name = "tensorstore", version = "0.1.78", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, - { name = "tensorstore", version = "0.1.80", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "tensorstore", version = "0.1.78", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "tensorstore", version = "0.1.80", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "treescope" }, { name = "typing-extensions" }, ] @@ -3000,8 +2989,7 @@ dependencies = [ { name = "google-cloud-core" }, { name = "google-resumable-media" }, { name = "packaging" }, - { name = "python-dateutil", version = "2.8.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-9-crfm-helm-human-evaluation'" }, - { name = "python-dateutil", version = "2.9.0.post0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-9-crfm-helm-ibm' or extra != 'extra-9-crfm-helm-human-evaluation'" }, + { name = "python-dateutil" }, { name = "requests" }, ] sdist = { url = "https://files.pythonhosted.org/packages/94/0a/62438ca138a095945468968696d9cca75a4cfd059e810402e70b0236d8ba/google_cloud_bigquery-3.40.0.tar.gz", hash = "sha256:b3ccb11caf0029f15b29569518f667553fe08f6f1459b959020c83fbbd8f2e68", size = 509287, upload-time = "2026-01-08T01:07:26.065Z" } @@ -3357,8 +3345,8 @@ name = "h5py" version = "3.15.1" source = { registry = "https://pypi.org/simple" } dependencies = [ 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wheels = [ @@ -3485,7 +3473,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "filelock" }, { name = "fsspec" }, - { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" }, { name = "packaging" }, { name = "pyyaml" }, { name = "requests" }, @@ -3502,7 +3490,7 @@ name = "humanfriendly" version = "10.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "pyreadline3", marker = "sys_platform == 'win32' or (extra == 'extra-9-crfm-helm-human-evaluation' and extra == 'extra-9-crfm-helm-ibm')" }, + { name = "pyreadline3", marker = "sys_platform == 'win32'" }, ] sdist = { url = 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17 00:00:00 2001 From: Yifan Mai Date: Tue, 10 Mar 2026 17:09:24 -0700 Subject: [PATCH 10/89] Modularize model deployment generators (#4103) --- .../benchmark/model_deployment_registry.py | 79 ++----------------- .../benchmark/model_deployments/__init__.py | 0 ...e_inference_providers_model_deployments.py | 28 +++++++ .../huggingface_model_deployments.py | 28 +++++++ .../litellm_model_deployments.py | 14 ++++ .../together_model_deployments.py | 10 +++ 6 files changed, 85 insertions(+), 74 deletions(-) create mode 100644 src/helm/benchmark/model_deployments/__init__.py create mode 100644 src/helm/benchmark/model_deployments/huggingface_inference_providers_model_deployments.py create mode 100644 src/helm/benchmark/model_deployments/huggingface_model_deployments.py create mode 100644 src/helm/benchmark/model_deployments/litellm_model_deployments.py create mode 100644 src/helm/benchmark/model_deployments/together_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index 58f18d123c1..ac992a56445 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -127,6 +127,11 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo deployment: Optional[ModelDeployment] = DEPLOYMENT_NAME_TO_MODEL_DEPLOYMENT.get(name) if not deployment: + import helm.benchmark.model_deployments.huggingface_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.huggingface_inference_providers_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.litellm_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 + for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): if name.startswith(prefix): deployment = model_deployment_generator(name) @@ -243,77 +248,3 @@ def wrap(func: F) -> F: return func return wrap - - -@model_deployment_generator("together") -def get_together_model_deployment(name: str): - name_parts = name.split("/") - model_name = "/".join(name_parts[-2:]) - return ModelDeployment( - name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.together_client.TogetherClient") - ) - - -@model_deployment_generator("litellm") -def get_litellm_model_deployment(name: str): - name_parts = name.split("/") - model_name = "/".join(name_parts[-2:]) - return ModelDeployment( - name=name, - model_name=model_name, - client_spec=ClientSpec( - "helm.clients.litellm_client.LiteLLMCompletionClient", args={"litellm_model": "/".join(name_parts[1:])} - ), - ) - - -@model_deployment_generator("huggingface") -def get_huggingface_model_deployment(name: str): - from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer - - name_parts = name.split("/") - model_name = "/".join(name_parts[-2:]) - pretrained_model_name_or_path = "/".join(name_parts[1:]) - with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: - max_sequence_length = tokenizer.model_max_length - if max_sequence_length > 1_000_000_000: - hwarn( - f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " - "input truncation may not work correctly; errors may result from exceeding the model's max length" - ) - return ModelDeployment( - name=name, - model_name=model_name, - client_spec=ClientSpec( - "helm.clients.huggingface_client.HuggingFaceClient", - args={"pretrained_model_name_or_path": pretrained_model_name_or_path}, - ), - tokenizer_name=name, - max_sequence_length=max_sequence_length, - ) - - -@model_deployment_generator("huggingface-inference-providers") -def get_huggingface_inference_providers_model_deployment(name: str): - from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer - - name_parts = name.split("/") - model_name = "/".join(name_parts[-2:]) - pretrained_model_name_or_path = "/".join(name_parts[1:]) - pretrained_model_name_or_path = pretrained_model_name_or_path.split(":")[0] - with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: - max_sequence_length = tokenizer.model_max_length - if max_sequence_length > 1_000_000_000: - hwarn( - f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " - "input truncation may not work correctly; errors may result from exceeding the model's max length" - ) - return ModelDeployment( - name=name, - model_name=model_name, - client_spec=ClientSpec( - "helm.clients.huggingface_inference_providers_client.HuggingFaceInferenceProvidersClient" - ), - tokenizer_name=f"huggingface/{pretrained_model_name_or_path}", - max_sequence_length=max_sequence_length, - ) diff --git a/src/helm/benchmark/model_deployments/__init__.py b/src/helm/benchmark/model_deployments/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/src/helm/benchmark/model_deployments/huggingface_inference_providers_model_deployments.py b/src/helm/benchmark/model_deployments/huggingface_inference_providers_model_deployments.py new file mode 100644 index 00000000000..e5e788a6dd5 --- /dev/null +++ b/src/helm/benchmark/model_deployments/huggingface_inference_providers_model_deployments.py @@ -0,0 +1,28 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator +from helm.common.hierarchical_logger import hwarn + + +@model_deployment_generator("huggingface-inference-providers") +def get_huggingface_inference_providers_model_deployment(name: str): + from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer + + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + pretrained_model_name_or_path = "/".join(name_parts[1:]) + pretrained_model_name_or_path = pretrained_model_name_or_path.split(":")[0] + with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: + max_sequence_length = tokenizer.model_max_length + if max_sequence_length > 1_000_000_000: + hwarn( + f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " + "input truncation may not work correctly; errors may result from exceeding the model's max length" + ) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.huggingface_inference_providers_client.HuggingFaceInferenceProvidersClient" + ), + tokenizer_name=f"huggingface/{pretrained_model_name_or_path}", + max_sequence_length=max_sequence_length, + ) diff --git a/src/helm/benchmark/model_deployments/huggingface_model_deployments.py b/src/helm/benchmark/model_deployments/huggingface_model_deployments.py new file mode 100644 index 00000000000..6e0dafe1da5 --- /dev/null +++ b/src/helm/benchmark/model_deployments/huggingface_model_deployments.py @@ -0,0 +1,28 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator +from helm.common.hierarchical_logger import hwarn + + +@model_deployment_generator("huggingface") +def get_huggingface_model_deployment(name: str): + from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer + + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + pretrained_model_name_or_path = "/".join(name_parts[1:]) + with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: + max_sequence_length = tokenizer.model_max_length + if max_sequence_length > 1_000_000_000: + hwarn( + f"Hugging Face model {pretrained_model_name_or_path} does not have a configured model_max_length; " + "input truncation may not work correctly; errors may result from exceeding the model's max length" + ) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.huggingface_client.HuggingFaceClient", + args={"pretrained_model_name_or_path": pretrained_model_name_or_path}, + ), + tokenizer_name=name, + max_sequence_length=max_sequence_length, + ) diff --git a/src/helm/benchmark/model_deployments/litellm_model_deployments.py b/src/helm/benchmark/model_deployments/litellm_model_deployments.py new file mode 100644 index 00000000000..c8dbfc42f09 --- /dev/null +++ b/src/helm/benchmark/model_deployments/litellm_model_deployments.py @@ -0,0 +1,14 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("litellm") +def get_litellm_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.litellm_client.LiteLLMCompletionClient", args={"litellm_model": "/".join(name_parts[1:])} + ), + ) diff --git a/src/helm/benchmark/model_deployments/together_model_deployments.py b/src/helm/benchmark/model_deployments/together_model_deployments.py new file mode 100644 index 00000000000..ccbdb1e004c --- /dev/null +++ b/src/helm/benchmark/model_deployments/together_model_deployments.py @@ -0,0 +1,10 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("together") +def get_together_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.together_client.TogetherClient") + ) From 932245a6f49fe4c2a0135708691264fab30f0811 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Mar 2026 18:04:20 -0700 Subject: [PATCH 11/89] Bump cryptography from 44.0.1 to 46.0.5 (#4105) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 731da71d0c3..ae9d4a70793 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,7 @@ colorcet==3.1.0 colorlog==6.10.1 confection==0.1.5 contourpy==1.3.2 -cryptography==44.0.1 +cryptography==46.0.5 cycler==0.12.1 cymem==2.0.13 dacite==1.9.2 From 0f124a6751600acbc23ffd7fff3f3d0fcb4af0d4 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 10 Mar 2026 18:04:49 -0700 Subject: [PATCH 12/89] Switch llama-4-scout-17b-16e-instruct to use Vertex AI instead of Together (#4104) --- src/helm/config/model_deployments.yaml | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index 9ded3285150..1b3f67e3f2c 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -1352,6 +1352,16 @@ model_deployments: args: vertexai_model: publishers/meta/models/llama-3.1-405b-instruct-maas + - name: google/llama-4-scout-17b-16e-instruct + model_name: meta/llama-4-scout-17b-16e-instruct + tokenizer_name: meta/llama-4-scout-17b-16e-instruct + max_sequence_length: 128000 + client_spec: + class_name: "helm.clients.vertexai_client.VertexAIChatClient" + args: + vertexai_model: publishers/meta/models/llama-4-scout-17b-16e-instruct-maas + genai_use_vertexai: False + ## Gemma - name: together/gemma-2b model_name: google/gemma-2b @@ -3419,15 +3429,6 @@ model_deployments: args: together_model: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo - - name: together/llama-4-scout-17b-16e-instruct - model_name: meta/llama-4-scout-17b-16e-instruct - tokenizer_name: meta/llama-4-scout-17b-16e-instruct - max_sequence_length: 327680 - client_spec: - class_name: "helm.clients.together_client.TogetherChatClient" - args: - together_model: meta-llama/Llama-4-Scout-17B-16E-Instruct - - name: together/llama-4-maverick-17b-128e-instruct-fp8 model_name: meta/llama-4-maverick-17b-128e-instruct-fp8 tokenizer_name: meta/llama-4-scout-17b-16e-instruct From 34ea961607ed00a3416180899d7203e002b73c5d Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 11 Mar 2026 14:09:09 -0700 Subject: [PATCH 13/89] Allow using OpenAI models without configuration (#4107) --- src/helm/benchmark/model_deployment_registry.py | 1 + .../model_deployments/openai_model_deployments.py | 12 ++++++++++++ 2 files changed, 13 insertions(+) create mode 100644 src/helm/benchmark/model_deployments/openai_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index ac992a56445..87d053303fa 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -130,6 +130,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo import helm.benchmark.model_deployments.huggingface_model_deployments # noqa: F401 import helm.benchmark.model_deployments.huggingface_inference_providers_model_deployments # noqa: F401 import helm.benchmark.model_deployments.litellm_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.openai_model_deployments # noqa: F401 import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): diff --git a/src/helm/benchmark/model_deployments/openai_model_deployments.py b/src/helm/benchmark/model_deployments/openai_model_deployments.py new file mode 100644 index 00000000000..b2e2adb985b --- /dev/null +++ b/src/helm/benchmark/model_deployments/openai_model_deployments.py @@ -0,0 +1,12 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("openai") +def get_openai_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec("helm.clients.openai_responses_client.OpenAIResponseClient"), + ) From aff1a1a378defaf78be847716c0a39724248241c Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 11 Mar 2026 14:12:12 -0700 Subject: [PATCH 14/89] Use TogetherChatClient instead of TogetherClient for generated model deployments (#4106) --- .../benchmark/model_deployments/together_model_deployments.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/helm/benchmark/model_deployments/together_model_deployments.py b/src/helm/benchmark/model_deployments/together_model_deployments.py index ccbdb1e004c..8a5e5801143 100644 --- a/src/helm/benchmark/model_deployments/together_model_deployments.py +++ b/src/helm/benchmark/model_deployments/together_model_deployments.py @@ -6,5 +6,5 @@ def get_together_model_deployment(name: str): name_parts = name.split("/") model_name = "/".join(name_parts[-2:]) return ModelDeployment( - name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.together_client.TogetherClient") + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.together_client.TogetherChatClient") ) From 6f4d25c887ba66b60aba3b16ce7497c7254d7926 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 11 Mar 2026 14:16:52 -0700 Subject: [PATCH 15/89] Remove tokenization from AnthropicMessagesClient (#4108) --- src/helm/clients/anthropic_client.py | 33 ++++++++++++++-------------- 1 file changed, 16 insertions(+), 17 deletions(-) diff --git a/src/helm/clients/anthropic_client.py b/src/helm/clients/anthropic_client.py index 893438e417f..0e89300974b 100644 --- a/src/helm/clients/anthropic_client.py +++ b/src/helm/clients/anthropic_client.py @@ -1,4 +1,3 @@ -import dataclasses from typing import Any, Dict, List, Optional, TypedDict, Union, cast import json import os @@ -28,7 +27,7 @@ ) from helm.proxy.retry import NonRetriableException from helm.tokenizers.tokenizer import Tokenizer -from helm.clients.client import CachingClient, truncate_sequence, truncate_and_tokenize_response_text +from helm.clients.client import CachingClient, truncate_sequence try: from anthropic import Anthropic, BadRequestError @@ -258,8 +257,6 @@ class AnthropicMessagesClient(CachingClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, thinking_budget_tokens: Optional[int] = None, anthropic_model_name: Optional[str] = None, @@ -267,8 +264,6 @@ def __init__( stream: Optional[bool] = None, ): super().__init__(cache_config=cache_config) - self.tokenizer = tokenizer - self.tokenizer_name = tokenizer_name self.client = Anthropic(api_key=api_key) self.api_key: Optional[str] = api_key self.anthropic_model_name: Optional[str] = anthropic_model_name @@ -450,21 +445,25 @@ def do_it() -> Dict[str, Any]: ) response_message: Message = Message.model_validate(raw_response) - response_text: Optional[str] = None - response_thinking: Optional[str] = None + text_blocks: List[str] = [] + thinking_blocks: List[str] = [] for content in response_message.content: if isinstance(content, TextBlock): - response_text = content.text + text_blocks.append(content.text) elif isinstance(content, ThinkingBlock): - response_thinking = content.thinking - if response_text is None: - raise Exception("Anthropic response did not contain text block") - completion = truncate_and_tokenize_response_text( - response_text, request, self.tokenizer, self.tokenizer_name, original_finish_reason="" + thinking_blocks.append(content.thinking) + if not text_blocks: + raise Exception("Anthropic response did not contain text blocks") + text_output = "".join(text_blocks) + thinking = Thinking(text="".join(thinking_blocks)) if thinking_blocks else None + completions.append( + GeneratedOutput( + text=text_output, + logprob=0.0, + tokens=[], + thinking=thinking, + ) ) - if response_thinking is not None: - completion = dataclasses.replace(completion, thinking=Thinking(text=response_thinking)) - completions.append(completion) return RequestResult( success=True, From b80479fcb9e3cddfb9d305d4f8e72387a4884a59 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 11 Mar 2026 14:37:28 -0700 Subject: [PATCH 16/89] Allow using Anthropic models without configuration (#4109) --- src/helm/benchmark/model_deployment_registry.py | 1 + .../model_deployments/anthropic_model_deployments.py | 12 ++++++++++++ 2 files changed, 13 insertions(+) create mode 100644 src/helm/benchmark/model_deployments/anthropic_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index 87d053303fa..c37b26d3f2b 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -127,6 +127,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo deployment: Optional[ModelDeployment] = DEPLOYMENT_NAME_TO_MODEL_DEPLOYMENT.get(name) if not deployment: + import helm.benchmark.model_deployments.anthropic_model_deployments # noqa: F401 import helm.benchmark.model_deployments.huggingface_model_deployments # noqa: F401 import helm.benchmark.model_deployments.huggingface_inference_providers_model_deployments # noqa: F401 import helm.benchmark.model_deployments.litellm_model_deployments # noqa: F401 diff --git a/src/helm/benchmark/model_deployments/anthropic_model_deployments.py b/src/helm/benchmark/model_deployments/anthropic_model_deployments.py new file mode 100644 index 00000000000..20b6b54408f --- /dev/null +++ b/src/helm/benchmark/model_deployments/anthropic_model_deployments.py @@ -0,0 +1,12 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("anthropic") +def get_anthropic_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec("helm.clients.anthropic_client.AnthropicMessagesClient"), + ) From 83c92dfb1812606a78c75a215ff3986b4409ede0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 12 Mar 2026 17:21:05 -0700 Subject: [PATCH 17/89] Bump @types/node from 25.3.3 to 25.4.0 in /helm-frontend in the npm group (#4110) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- helm-frontend/package.json | 2 +- helm-frontend/yarn.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/helm-frontend/package.json b/helm-frontend/package.json index bc706465939..50e94c1836b 100644 --- a/helm-frontend/package.json +++ b/helm-frontend/package.json @@ -31,7 +31,7 @@ "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^14.3.1", "@testing-library/user-event": "^14.6.1", - "@types/node": "^25.3.3", + "@types/node": "^25.4.0", "@types/react": "^18.2.15", "@types/react-dom": "^18.2.7", "@types/react-helmet": "^6.1.11", diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index a9f02436fdd..0ce437d3d80 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -1508,10 +1508,10 @@ resolved "https://registry.yarnpkg.com/@types/ms/-/ms-0.7.32.tgz#f6cd08939ae3ad886fcc92ef7f0109dacddf61ab" integrity sha512-xPSg0jm4mqgEkNhowKgZFBNtwoEwF6gJ4Dhww+GFpm3IgtNseHQZ5IqdNwnquZEoANxyDAKDRAdVo4Z72VvD/g== -"@types/node@*", "@types/node@^25.3.3": - version "25.3.3" - resolved "https://registry.yarnpkg.com/@types/node/-/node-25.3.3.tgz#605862544ee7ffd7a936bcbf0135a14012f1e549" - integrity sha512-DpzbrH7wIcBaJibpKo9nnSQL0MTRdnWttGyE5haGwK86xgMOkFLp7vEyfQPGLOJh5wNYiJ3V9PmUMDhV9u8kkQ== +"@types/node@*", "@types/node@^25.4.0": + version "25.4.0" + resolved "https://registry.yarnpkg.com/@types/node/-/node-25.4.0.tgz#f25d8467984d6667cc4c1be1e2f79593834aaedb" + integrity sha512-9wLpoeWuBlcbBpOY3XmzSTG3oscB6xjBEEtn+pYXTfhyXhIxC5FsBer2KTopBlvKEiW9l13po9fq+SJY/5lkhw== dependencies: undici-types "~7.18.0" From 91d2e5d79e3a4d207fc58cb39c2d0df39949f732 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 12 Mar 2026 17:43:15 -0700 Subject: [PATCH 18/89] Rename GitHub Action workflows (#4111) --- .../{frontend.yml => deploy-frontend.yaml} | 38 +------------------ .../{python-publish.yml => publish-pypi.yml} | 0 .github/workflows/test-frontend.yml | 37 ++++++++++++++++++ .../workflows/{test.yml => test-python.yml} | 2 +- ...{test_scenarios.yml => test-scenarios.yml} | 2 +- 5 files changed, 41 insertions(+), 38 deletions(-) rename .github/workflows/{frontend.yml => deploy-frontend.yaml} (63%) rename .github/workflows/{python-publish.yml => publish-pypi.yml} (100%) create mode 100644 .github/workflows/test-frontend.yml rename .github/workflows/{test.yml => test-python.yml} (99%) rename .github/workflows/{test_scenarios.yml => test-scenarios.yml} (97%) diff --git a/.github/workflows/frontend.yml b/.github/workflows/deploy-frontend.yaml similarity index 63% rename from .github/workflows/frontend.yml rename to .github/workflows/deploy-frontend.yaml index 5c5111774ff..190aa362e64 100644 --- a/.github/workflows/frontend.yml +++ b/.github/workflows/deploy-frontend.yaml @@ -1,47 +1,13 @@ -name: Frontend +name: Deploy Frontend -on: - push: - branches: - - '*' - paths: - - 'helm-frontend/**' - pull_request: - branches: - - '*' - paths: - - 'helm-frontend/**' +on: workflow_dispatch jobs: - test: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v4 - - name: Use Node.js - uses: actions/setup-node@v4 - with: - node-version: '18' - - name: Install Yarn - run: npm install --global yarn - - name: Install dependencies - working-directory: ./helm-frontend - run: yarn install - - name: Run pre-commit - run: ./pre-commit-frontend.sh - - name: Build - working-directory: ./helm-frontend - run: yarn build - - name: Run tests - working-directory: ./helm-frontend - run: yarn test - build: runs-on: ubuntu-latest # Deploy to only run on pushes to master # if: github.event_name == 'push' && github.ref == 'refs/heads/main' if: github.event_name == 'push' && github.ref == 'refs/heads/react_frontend' - needs: test environment: name: github-pages env: diff --git a/.github/workflows/python-publish.yml b/.github/workflows/publish-pypi.yml similarity index 100% rename from .github/workflows/python-publish.yml rename to .github/workflows/publish-pypi.yml diff --git a/.github/workflows/test-frontend.yml b/.github/workflows/test-frontend.yml new file mode 100644 index 00000000000..4b313cf95a8 --- /dev/null +++ b/.github/workflows/test-frontend.yml @@ -0,0 +1,37 @@ +name: Test Frontend + +on: + push: + branches: + - '*' + paths: + - 'helm-frontend/**' + pull_request: + branches: + - '*' + paths: + - 'helm-frontend/**' + +jobs: + test: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + - name: Use Node.js + uses: actions/setup-node@v4 + with: + node-version: '18' + - name: Install Yarn + run: npm install --global yarn + - name: Install dependencies + working-directory: ./helm-frontend + run: yarn install + - name: Run pre-commit + run: ./pre-commit-frontend.sh + - name: Build + working-directory: ./helm-frontend + run: yarn build + - name: Run tests + working-directory: ./helm-frontend + run: yarn test diff --git a/.github/workflows/test.yml b/.github/workflows/test-python.yml similarity index 99% rename from .github/workflows/test.yml rename to .github/workflows/test-python.yml index f8486a41dc3..e7d2009948e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test-python.yml @@ -1,4 +1,4 @@ -name: Test +name: Test Python on: push: branches: [ main ] diff --git a/.github/workflows/test_scenarios.yml b/.github/workflows/test-scenarios.yml similarity index 97% rename from .github/workflows/test_scenarios.yml rename to .github/workflows/test-scenarios.yml index 39dbda9d579..ec8ad299b05 100644 --- a/.github/workflows/test_scenarios.yml +++ b/.github/workflows/test-scenarios.yml @@ -1,4 +1,4 @@ -name: Scenario tests +name: Test Scenarios on: push: branches: [ main ] From 785d256d78b701295756814674730f2f23f3dae8 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 13 Mar 2026 11:53:55 -0700 Subject: [PATCH 19/89] Remove tokenization from OpenAIClient (#4113) --- src/helm/clients/azure_openai_client.py | 5 --- src/helm/clients/grok_client.py | 5 --- src/helm/clients/nvidia_nim_client.py | 7 ---- src/helm/clients/openai_client.py | 37 ++++++++----------- src/helm/clients/openrouter_client.py | 7 +--- src/helm/clients/palmyra_client.py | 29 +++------------ .../stanfordhealthcare_azure_openai_client.py | 7 ---- .../stanfordhealthcare_openai_client.py | 5 --- .../stanfordhealthcare_shc_openai_client.py | 5 --- src/helm/clients/test_openrouter_client.py | 13 ------- src/helm/clients/upstage_client.py | 5 --- src/helm/clients/vllm_client.py | 7 ---- src/helm/clients/yi_client.py | 7 ---- 13 files changed, 23 insertions(+), 116 deletions(-) diff --git a/src/helm/clients/azure_openai_client.py b/src/helm/clients/azure_openai_client.py index 25c3da876a7..895b65bad10 100644 --- a/src/helm/clients/azure_openai_client.py +++ b/src/helm/clients/azure_openai_client.py @@ -5,7 +5,6 @@ from helm.common.cache import CacheConfig from helm.common.optional_dependencies import handle_module_not_found_error from helm.proxy.retry import NonRetriableException -from helm.tokenizers.tokenizer import Tokenizer try: from openai import AzureOpenAI @@ -18,8 +17,6 @@ class AzureOpenAIClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, endpoint: Optional[str] = None, @@ -29,8 +26,6 @@ def __init__( azure_openai_deployment_name: Optional[str] = None, ): super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key="unused", openai_model_name=azure_openai_deployment_name, diff --git a/src/helm/clients/grok_client.py b/src/helm/clients/grok_client.py index ac1bb3f90dd..08465a8403a 100644 --- a/src/helm/clients/grok_client.py +++ b/src/helm/clients/grok_client.py @@ -3,7 +3,6 @@ from helm.clients.openai_client import OpenAIClient from helm.common.cache import CacheConfig from helm.common.request import Request -from helm.tokenizers.tokenizer import Tokenizer class GrokChatClient(OpenAIClient): @@ -14,14 +13,10 @@ class GrokChatClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, ): super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, org_id=None, diff --git a/src/helm/clients/nvidia_nim_client.py b/src/helm/clients/nvidia_nim_client.py index ba632f3ef6b..62fd682b04c 100644 --- a/src/helm/clients/nvidia_nim_client.py +++ b/src/helm/clients/nvidia_nim_client.py @@ -3,7 +3,6 @@ from helm.clients.openai_client import OpenAIClient from helm.common.cache import CacheConfig from helm.common.request import Request -from helm.tokenizers.tokenizer import Tokenizer class NvidiaNimClient(OpenAIClient): @@ -12,16 +11,10 @@ class NvidiaNimClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, ): - self.tokenizer = tokenizer - self.tokenizer_name = tokenizer_name super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, org_id=None, diff --git a/src/helm/clients/openai_client.py b/src/helm/clients/openai_client.py index 4db89fefd1f..179c72ab0b5 100644 --- a/src/helm/clients/openai_client.py +++ b/src/helm/clients/openai_client.py @@ -1,7 +1,7 @@ # mypy: check_untyped_defs = False from dataclasses import replace import re -from typing import Any, Dict, List, Optional, cast, Union, Callable +from typing import Any, Dict, List, Optional, Union, Callable from openai import OpenAIError @@ -13,12 +13,7 @@ from helm.common.hierarchical_logger import hlog, hwarn, hexception from helm.common.object_spec import get_class_by_name from helm.common.optional_dependencies import handle_module_not_found_error -from helm.common.tokenization_request import ( - TokenizationRequest, - TokenizationRequestResult, -) from helm.clients.client import Client, CachingClient, truncate_sequence, generate_uid_for_multimodal_prompt -from helm.tokenizers.tokenizer import Tokenizer try: import openai @@ -122,8 +117,6 @@ class OpenAIClient(CachingClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, org_id: Optional[str] = None, @@ -134,8 +127,6 @@ def __init__( **kwargs, ): super().__init__(cache_config=cache_config) - self.tokenizer = tokenizer - self.tokenizer_name = tokenizer_name self.client = OpenAI(api_key=api_key, organization=org_id, base_url=base_url, **kwargs) self.reasoning_effort = reasoning_effort self.openai_model_name = openai_model_name @@ -370,17 +361,23 @@ def do_it() -> Dict[str, Any]: if self.output_processor: raw_completion_content = self.output_processor(raw_completion_content) text: str = request.prompt + raw_completion_content if request.echo_prompt else raw_completion_content - # The OpenAI chat completion API doesn't return us tokens or logprobs, so we tokenize ourselves. - tokenization_result: TokenizationRequestResult = self.tokenizer.tokenize( - TokenizationRequest(text, tokenizer=self.tokenizer_name) - ) # Log probs are not currently not supported by the OpenAI chat completion API, so set to 0 for now. - tokens: List[Token] = [ - Token(text=cast(str, raw_token), logprob=0) for raw_token in tokenization_result.raw_tokens - ] # vLLM has a optional `reasoning_content` field in the message # that is not in the standard OpenAI API. # This field is also used by some model providers such as Grok. + tokens: List[Token] = [] + if "logprobs" in raw_completion and raw_completion["logprobs"]: + raw_completion_logprobs = raw_completion["logprobs"] + if "content" in raw_completion_logprobs and raw_completion_logprobs["contents"]: + for raw_completion_token in raw_completion_logprobs["contents"]: + tokens.append( + Token( + text=raw_completion_token["token"], + logprob=raw_completion_token["logprob"], + ) + ) + logprob = sum([token.logprob for token in tokens]) + thinking = ( Thinking(text=raw_completion["message"]["reasoning_content"]) if "reasoning_content" in raw_completion["message"] @@ -388,7 +385,7 @@ def do_it() -> Dict[str, Any]: ) completion = GeneratedOutput( text=text, - logprob=0, # OpenAI does not provide logprobs + logprob=logprob, tokens=tokens, finish_reason={"reason": raw_completion["finish_reason"]}, thinking=thinking, @@ -533,15 +530,11 @@ def wrap_transcribed_indicator(transcription: str) -> str: def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, org_id: Optional[str] = None, ): self._openai_client = OpenAIClient( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, org_id=org_id, diff --git a/src/helm/clients/openrouter_client.py b/src/helm/clients/openrouter_client.py index 54e5d940e4f..c40c0990663 100644 --- a/src/helm/clients/openrouter_client.py +++ b/src/helm/clients/openrouter_client.py @@ -2,28 +2,25 @@ from typing import Optional from helm.clients.openai_client import OpenAIClient from helm.common.cache import CacheConfig -from helm.tokenizers.tokenizer import Tokenizer class OpenRouterClient(OpenAIClient): def __init__( self, - tokenizer_name: str, - tokenizer: Tokenizer, cache_config: CacheConfig, api_key: Optional[str] = None, model_name: Optional[str] = None, output_processor: Optional[str] = None, + openrouter_model_name: Optional[str] = None, ): self.api_key = api_key or os.getenv("OPENROUTER_API_KEY") self.base_url = "https://openrouter.ai/api/v1/" super().__init__( - tokenizer, - tokenizer_name, cache_config=cache_config, output_processor=output_processor, base_url=self.base_url, api_key=self.api_key, + openai_model_name=openrouter_model_name, ) self.model_name = model_name diff --git a/src/helm/clients/palmyra_client.py b/src/helm/clients/palmyra_client.py index f4fa75e049e..3b7c5eeac90 100644 --- a/src/helm/clients/palmyra_client.py +++ b/src/helm/clients/palmyra_client.py @@ -6,12 +6,7 @@ from helm.clients.openai_client import OpenAIClient from helm.common.cache import CacheConfig from helm.common.hierarchical_logger import hexception, hwarn -from helm.common.request import wrap_request_time, Request, RequestResult, GeneratedOutput, Token, ErrorFlags -from helm.common.tokenization_request import ( - TokenizationRequest, - TokenizationRequestResult, -) -from helm.tokenizers.tokenizer import Tokenizer +from helm.common.request import wrap_request_time, Request, RequestResult, GeneratedOutput, ErrorFlags from helm.clients.client import CachingClient, truncate_sequence @@ -29,11 +24,9 @@ def _is_content_moderation_failure(response: Dict) -> bool: class PalmyraClient(CachingClient): - def __init__(self, tokenizer: Tokenizer, tokenizer_name: str, cache_config: CacheConfig, api_key: str): + def __init__(self, cache_config: CacheConfig, api_key: str): super().__init__(cache_config=cache_config) self.api_key: str = api_key - self.tokenizer = tokenizer - self.tokenizer_name = tokenizer_name def _send_request(self, model_name: str, raw_request: Dict[str, Any]) -> Dict[str, Any]: response = requests.request( @@ -119,17 +112,11 @@ def do_it() -> Dict[str, Any]: response_text: str = response["choices"][0]["text"] # The Writer API doesn't support echo. If `echo_prompt` is true, combine the prompt and completion. - text: str = request.prompt + response_text if request.echo_prompt else response_text - # The Writer API doesn't return us tokens or logprobs, so we tokenize ourselves. - tokenization_result: TokenizationRequestResult = self.tokenizer.tokenize( - # Writer uses the GPT-2 tokenizer - TokenizationRequest(text, tokenizer=self.tokenizer_name) - ) - - # Log probs are not currently not supported by the Writer, so set to 0 for now. - tokens: List[Token] = [Token(text=str(text), logprob=0) for text in tokenization_result.raw_tokens] + if request.echo_prompt: + response_text = request.prompt + response_text - completion = GeneratedOutput(text=response_text, logprob=0, tokens=tokens) + # The Writer API doesn't return us tokens or logprobs, so we tokenize ourselves. + completion = GeneratedOutput(text=response_text, logprob=0, tokens=[]) sequence = truncate_sequence(completion, request, print_warning=True) completions.append(sequence) @@ -148,14 +135,10 @@ class PalmyraChatClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: str, ): super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, org_id=None, diff --git a/src/helm/clients/stanfordhealthcare_azure_openai_client.py b/src/helm/clients/stanfordhealthcare_azure_openai_client.py index 0d5d230f7c3..551171399d2 100644 --- a/src/helm/clients/stanfordhealthcare_azure_openai_client.py +++ b/src/helm/clients/stanfordhealthcare_azure_openai_client.py @@ -3,7 +3,6 @@ from helm.clients.azure_openai_client import AzureOpenAIClient from helm.common.cache import CacheConfig from helm.proxy.retry import NonRetriableException -from helm.tokenizers.tokenizer import Tokenizer class StanfordHealthCareAzureOpenAIClient(AzureOpenAIClient): @@ -22,8 +21,6 @@ class StanfordHealthCareAzureOpenAIClient(AzureOpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, openai_model_name: str, api_version: str, @@ -36,8 +33,6 @@ def __init__( if base_url: base_url = base_url.format(endpoint=endpoint) super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, base_url=base_url, @@ -47,8 +42,6 @@ def __init__( ) else: super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, endpoint=endpoint, diff --git a/src/helm/clients/stanfordhealthcare_openai_client.py b/src/helm/clients/stanfordhealthcare_openai_client.py index c4538ad2289..58e7df9f4b6 100644 --- a/src/helm/clients/stanfordhealthcare_openai_client.py +++ b/src/helm/clients/stanfordhealthcare_openai_client.py @@ -4,7 +4,6 @@ from helm.common.cache import CacheConfig from helm.common.optional_dependencies import handle_module_not_found_error from helm.proxy.retry import NonRetriableException -from helm.tokenizers.tokenizer import Tokenizer try: from openai import OpenAI @@ -28,8 +27,6 @@ class StanfordHealthCareOpenAIClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, model_name: str, api_key: Optional[str] = None, @@ -39,8 +36,6 @@ def __init__( output_processor: Optional[str] = None, ): super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key="unused", openai_model_name=openai_model_name, diff --git a/src/helm/clients/stanfordhealthcare_shc_openai_client.py b/src/helm/clients/stanfordhealthcare_shc_openai_client.py index 8209f675a9e..16ab7fa202e 100644 --- a/src/helm/clients/stanfordhealthcare_shc_openai_client.py +++ b/src/helm/clients/stanfordhealthcare_shc_openai_client.py @@ -3,7 +3,6 @@ from helm.clients.azure_openai_client import AzureOpenAIClient from helm.common.cache import CacheConfig from helm.proxy.retry import NonRetriableException -from helm.tokenizers.tokenizer import Tokenizer class StanfordHealthCareSHCOpenAIClient(AzureOpenAIClient): @@ -22,8 +21,6 @@ class StanfordHealthCareSHCOpenAIClient(AzureOpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, endpoint: Optional[str] = None, @@ -32,8 +29,6 @@ def __init__( if not api_key: raise NonRetriableException("Must provide API key through credentials.conf") super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, endpoint=endpoint, diff --git a/src/helm/clients/test_openrouter_client.py b/src/helm/clients/test_openrouter_client.py index e282d8de10a..b460bf8465d 100644 --- a/src/helm/clients/test_openrouter_client.py +++ b/src/helm/clients/test_openrouter_client.py @@ -6,18 +6,11 @@ from helm.common.request import Request from helm.clients.openrouter_client import OpenRouterClient -from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer - class TestOpenRouterClient: def setup_method(self, method): cache_file = tempfile.NamedTemporaryFile(delete=False) self.cache_path: str = cache_file.name - self.tokenizer_name = "mistralai/Mistral-7B-v0.1" - self.tokenizer = HuggingFaceTokenizer( - cache_config=BlackHoleCacheConfig(), - tokenizer_name=self.tokenizer_name, - ) def teardown_method(self, method): os.remove(self.cache_path) @@ -42,8 +35,6 @@ def teardown_method(self, method): ) def test_get_model_for_request(self, model_name, test_input, expected_model): client = OpenRouterClient( - tokenizer_name=self.tokenizer_name, - tokenizer=self.tokenizer, cache_config=SqliteCacheConfig(self.cache_path), model_name=model_name, api_key="test_key", @@ -53,16 +44,12 @@ def test_get_model_for_request(self, model_name, test_input, expected_model): def test_api_key_env_var(self, monkeypatch): monkeypatch.setenv("OPENROUTER_API_KEY", "test_key") client = OpenRouterClient( - tokenizer_name=self.tokenizer_name, - tokenizer=self.tokenizer, cache_config=SqliteCacheConfig(self.cache_path), ) assert client.api_key == "test_key" def test_api_key_argument(self): client = OpenRouterClient( - tokenizer_name=self.tokenizer_name, - tokenizer=self.tokenizer, cache_config=BlackHoleCacheConfig(), api_key="explicit_key", ) diff --git a/src/helm/clients/upstage_client.py b/src/helm/clients/upstage_client.py index 734acc4c3b2..3ea0cc44454 100644 --- a/src/helm/clients/upstage_client.py +++ b/src/helm/clients/upstage_client.py @@ -1,6 +1,5 @@ from helm.clients.openai_client import OpenAIClient from helm.common.cache import CacheConfig -from helm.tokenizers.tokenizer import Tokenizer class UpstageChatClient(OpenAIClient): @@ -8,14 +7,10 @@ class UpstageChatClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: str, ): super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, org_id=None, diff --git a/src/helm/clients/vllm_client.py b/src/helm/clients/vllm_client.py index 24f091c3240..2c0961dc084 100644 --- a/src/helm/clients/vllm_client.py +++ b/src/helm/clients/vllm_client.py @@ -34,7 +34,6 @@ def __init__( ) self.tokenizer = tokenizer self.tokenizer_name = tokenizer_name - self.vllm_model_name = vllm_model_name def _to_raw_completion_request(self, request: Request) -> Dict[str, Any]: raw_request = super()._to_raw_completion_request(request) @@ -58,16 +57,12 @@ class VLLMChatClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, base_url: Optional[str] = None, vllm_model_name: Optional[str] = None, **kwargs, ): super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key="EMPTY", org_id=None, @@ -75,6 +70,4 @@ def __init__( openai_model_name=vllm_model_name, **kwargs, ) - self.tokenizer = tokenizer - self.tokenizer_name = tokenizer_name self.vllm_model_name = vllm_model_name diff --git a/src/helm/clients/yi_client.py b/src/helm/clients/yi_client.py index 9bba787e96a..5f06e2bdc01 100644 --- a/src/helm/clients/yi_client.py +++ b/src/helm/clients/yi_client.py @@ -2,7 +2,6 @@ from helm.clients.openai_client import OpenAIClient from helm.common.cache import CacheConfig -from helm.tokenizers.tokenizer import Tokenizer class YiChatClient(OpenAIClient): @@ -11,16 +10,10 @@ class YiChatClient(OpenAIClient): def __init__( self, - tokenizer: Tokenizer, - tokenizer_name: str, cache_config: CacheConfig, api_key: Optional[str] = None, ): - self.tokenizer = tokenizer - self.tokenizer_name = tokenizer_name super().__init__( - tokenizer=tokenizer, - tokenizer_name=tokenizer_name, cache_config=cache_config, api_key=api_key, org_id=None, From 00e9ad4cccd1eae6ce87ff3a051f969bf375b26d Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 13 Mar 2026 12:03:18 -0700 Subject: [PATCH 20/89] Remove completed TODOs in pyproject.toml (#4112) --- pyproject.toml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c01cc7be109..26f35f12f85 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,8 +35,6 @@ dependencies = [ "spacy~=3.5", "tqdm~=4.64", "zstandard~=0.18.0", - # sqlitedict==2.0.0 is slow! https://github.com/RaRe-Technologies/sqlitedict/issues/152 - # Keep sqlitedict version at 1.7.0. "sqlitedict>=2.1.0,<3.0", "bottle~=0.12.23", # Basic Scenarios @@ -50,9 +48,7 @@ dependencies = [ "uncertainty-calibration~=0.1.4", "scikit-learn>=1.1", # Models and Metrics Extras - # TODO(#3731): Remove the bounds on transformers version after #3731 is resolved. "transformers~=4.53", # For anthropic_client, vision_language.huggingface_vlm_client, huggingface_client, huggingface_tokenizer, test_openai_token_cost_estimator, model_summac (via summarization_metrics) - # TODO: Upgrade torch - we need > 2.0.0 for newer versions of transformers "torch>=1.13.1,<3.0.0", # For huggingface_client, yalm_tokenizer, model_summac (via summarization_metrics) "torchvision>=0.14.1,<3.0.0", # For huggingface_client, yalm_tokenizer, model_summac (via summarization_metrics) ] @@ -508,4 +504,3 @@ markers = [ # Settings for Black: The Uncompromising Code Formatter [tool.black] line-length = 120 - From 1178c7904524308dbd6fa990e7f198ef2fbee21b Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 13 Mar 2026 12:10:28 -0700 Subject: [PATCH 21/89] Allow using OpenRouter models without configuration (#4114) --- src/helm/benchmark/model_deployment_registry.py | 1 + .../openrouter_model_deployments.py | 17 +++++++++++++++++ src/helm/benchmark/model_metadata_registry.py | 6 +++++- 3 files changed, 23 insertions(+), 1 deletion(-) create mode 100644 src/helm/benchmark/model_deployments/openrouter_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index c37b26d3f2b..589a610bbc6 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -132,6 +132,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo import helm.benchmark.model_deployments.huggingface_inference_providers_model_deployments # noqa: F401 import helm.benchmark.model_deployments.litellm_model_deployments # noqa: F401 import helm.benchmark.model_deployments.openai_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.openrouter_model_deployments # noqa: F401 import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): diff --git a/src/helm/benchmark/model_deployments/openrouter_model_deployments.py b/src/helm/benchmark/model_deployments/openrouter_model_deployments.py new file mode 100644 index 00000000000..3ced997e068 --- /dev/null +++ b/src/helm/benchmark/model_deployments/openrouter_model_deployments.py @@ -0,0 +1,17 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("openrouter") +def get_openrouter_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.openrouter_client.OpenRouterClient", + args={ + "openrouter_model_name": model_name, + }, + ), + ) diff --git a/src/helm/benchmark/model_metadata_registry.py b/src/helm/benchmark/model_metadata_registry.py index 4d5267dfc85..575fb24652c 100644 --- a/src/helm/benchmark/model_metadata_registry.py +++ b/src/helm/benchmark/model_metadata_registry.py @@ -191,7 +191,11 @@ def get_model_names_with_tag(tag: str) -> List[str]: def model_has_tag(model_name: str, tag: str) -> bool: """Return True if the model has the given tag. False otherwise.""" - return tag in get_model_metadata(model_name).tags + try: + tags = get_model_metadata(model_name).tags + except ValueError: + tags = [] + return tag in tags def get_all_text_models() -> List[str]: From e2db9e79af3bd3afd03061626a0d3a65db00831e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Mar 2026 13:52:44 -0700 Subject: [PATCH 22/89] Bump pyasn1 from 0.6.2 to 0.6.3 (#4116) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index ae9d4a70793..d2a1be49a60 100644 --- a/requirements.txt +++ b/requirements.txt @@ -112,7 +112,7 @@ proto-plus==1.27.0 protobuf==6.33.5 pyarrow==23.0.0 pyarrow-hotfix==0.7 -pyasn1==0.6.2 +pyasn1==0.6.3 pyasn1-modules==0.4.2 pycodestyle==2.14.0 pycparser==2.23 From 1e08ddf9a5f99119a710f97fc627c48886f93c2f Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 17 Mar 2026 16:32:48 -0700 Subject: [PATCH 23/89] Modularize tokenizer config generators (#4117) --- .../benchmark/tokenizer_config_registry.py | 63 ++++++++++--------- .../benchmark/tokenizer_configs/__init__.py | 0 .../huggingface_tokenizer_configs.py | 21 +++++++ 3 files changed, 54 insertions(+), 30 deletions(-) create mode 100644 src/helm/benchmark/tokenizer_configs/__init__.py create mode 100644 src/helm/benchmark/tokenizer_configs/huggingface_tokenizer_configs.py diff --git a/src/helm/benchmark/tokenizer_config_registry.py b/src/helm/benchmark/tokenizer_config_registry.py index 82b413508c5..9480cf516a1 100644 --- a/src/helm/benchmark/tokenizer_config_registry.py +++ b/src/helm/benchmark/tokenizer_config_registry.py @@ -1,4 +1,4 @@ -from typing import Dict, Optional, List +from typing import Callable, Dict, Optional, List, TypeVar from dataclasses import dataclass import cattrs @@ -52,34 +52,37 @@ def register_tokenizer_configs_from_path(path: str) -> None: register_tokenizer_config(tokenizer_config) -def auto_generate_tokenizer_config(name: str) -> TokenizerConfig: - name_parts = name.split("/") - name_base = name_parts[0] - if name_base == "huggingface": - from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer - - pretrained_model_name_or_path = "/".join(name_parts[1:]) - with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: - end_of_text_token = tokenizer.eos_token or "" - prefix_token = tokenizer.bos_token or "" - return TokenizerConfig( - name=name, - tokenizer_spec=TokenizerSpec( - "helm.tokenizers.huggingface_tokenizer.HuggingFaceTokenizer", - args={"pretrained_model_name_or_path": pretrained_model_name_or_path}, - ), - end_of_text_token=end_of_text_token, - prefix_token=prefix_token, - ) - else: - raise NotImplementedError(f"Could not auto generate tokenizer config for tokenizer {name}") +def get_tokenizer_config(name: str) -> Optional[TokenizerConfig]: + tokenizer_config: Optional[TokenizerConfig] = TOKENIZER_NAME_TO_CONFIG.get(name) + if not tokenizer_config: + import helm.benchmark.tokenizer_configs.huggingface_tokenizer_configs # noqa: F401 + for prefix, tokenizer_config_generator in _REGISTERED_TOKENIZER_CONFIG_GENERATORS.items(): + if name.startswith(prefix): + tokenizer_config = tokenizer_config_generator(name) -def get_tokenizer_config(name: str) -> Optional[TokenizerConfig]: - tokenizer_config = TOKENIZER_NAME_TO_CONFIG.get(name) - if tokenizer_config: - return tokenizer_config - name_parts = name.split("/") - if len(name_parts) > 2: - return auto_generate_tokenizer_config(name) - return None + return tokenizer_config + + +TokenizerConfigGenerator = Callable[[str], TokenizerConfig] +"""A function that takes in a model name and returns a TokenizerConfig""" + + +_REGISTERED_TOKENIZER_CONFIG_GENERATORS: Dict[str, TokenizerConfigGenerator] = {} +"""Dict of prefixes to TokenizerConfigGenerators.""" + + +F = TypeVar("F", bound=TokenizerConfigGenerator) + + +def tokenizer_config_generator(prefix: str) -> Callable[[F], F]: + """Register the TokenizerConfigGenerator for the given model name prefix.""" + + def wrap(func: F) -> F: + key = prefix.strip("/") + "/" + if key in _REGISTERED_TOKENIZER_CONFIG_GENERATORS: + raise ValueError(f"A TokenizerConfigGenerator with prefix {key} already exists") + _REGISTERED_TOKENIZER_CONFIG_GENERATORS[key] = func + return func + + return wrap diff --git a/src/helm/benchmark/tokenizer_configs/__init__.py b/src/helm/benchmark/tokenizer_configs/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/src/helm/benchmark/tokenizer_configs/huggingface_tokenizer_configs.py b/src/helm/benchmark/tokenizer_configs/huggingface_tokenizer_configs.py new file mode 100644 index 00000000000..f209f386a72 --- /dev/null +++ b/src/helm/benchmark/tokenizer_configs/huggingface_tokenizer_configs.py @@ -0,0 +1,21 @@ +from helm.benchmark.tokenizer_config_registry import TokenizerConfig, TokenizerSpec, tokenizer_config_generator + + +@tokenizer_config_generator("huggingface") +def get_huggingface_tokenizer_config(name: str): + from helm.tokenizers.huggingface_tokenizer import HuggingFaceTokenizer + + name_parts = name.split("/") + pretrained_model_name_or_path = "/".join(name_parts[1:]) + with HuggingFaceTokenizer.create_tokenizer(pretrained_model_name_or_path) as tokenizer: + end_of_text_token = tokenizer.eos_token or "" + prefix_token = tokenizer.bos_token or "" + return TokenizerConfig( + name=name, + tokenizer_spec=TokenizerSpec( + "helm.tokenizers.huggingface_tokenizer.HuggingFaceTokenizer", + args={"pretrained_model_name_or_path": pretrained_model_name_or_path}, + ), + end_of_text_token=end_of_text_token, + prefix_token=prefix_token, + ) From d639d03cf6ec8080a9e32958e19f23fedcc4a98c Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 17 Mar 2026 16:32:56 -0700 Subject: [PATCH 24/89] Add Mistral model deployments (#4118) --- src/helm/benchmark/model_deployment_registry.py | 1 + .../model_deployments/mistralai_model_deployments.py | 10 ++++++++++ 2 files changed, 11 insertions(+) create mode 100644 src/helm/benchmark/model_deployments/mistralai_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index 589a610bbc6..1f582662fff 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -131,6 +131,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo import helm.benchmark.model_deployments.huggingface_model_deployments # noqa: F401 import helm.benchmark.model_deployments.huggingface_inference_providers_model_deployments # noqa: F401 import helm.benchmark.model_deployments.litellm_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.mistralai_model_deployments # noqa: F401 import helm.benchmark.model_deployments.openai_model_deployments # noqa: F401 import helm.benchmark.model_deployments.openrouter_model_deployments # noqa: F401 import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 diff --git a/src/helm/benchmark/model_deployments/mistralai_model_deployments.py b/src/helm/benchmark/model_deployments/mistralai_model_deployments.py new file mode 100644 index 00000000000..5ee22b179ce --- /dev/null +++ b/src/helm/benchmark/model_deployments/mistralai_model_deployments.py @@ -0,0 +1,10 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("mistralai") +def get_together_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.mistral_client.MistralAIClient") + ) From 92c0216cc53a505ebe67acf45efb7ba51bfdd234 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 17 Mar 2026 16:33:04 -0700 Subject: [PATCH 25/89] Add Writer model deployments (#4119) --- src/helm/benchmark/model_deployment_registry.py | 1 + .../model_deployments/writer_model_deployments.py | 10 ++++++++++ src/helm/clients/writer_client.py | 2 +- 3 files changed, 12 insertions(+), 1 deletion(-) create mode 100644 src/helm/benchmark/model_deployments/writer_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index 1f582662fff..5a033feb5ce 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -135,6 +135,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo import helm.benchmark.model_deployments.openai_model_deployments # noqa: F401 import helm.benchmark.model_deployments.openrouter_model_deployments # noqa: F401 import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.writer_model_deployments # noqa: F401 for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): if name.startswith(prefix): diff --git a/src/helm/benchmark/model_deployments/writer_model_deployments.py b/src/helm/benchmark/model_deployments/writer_model_deployments.py new file mode 100644 index 00000000000..9f1428f6e6d --- /dev/null +++ b/src/helm/benchmark/model_deployments/writer_model_deployments.py @@ -0,0 +1,10 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("writer") +def get_writer_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.writer_client.WriterClient") + ) diff --git a/src/helm/clients/writer_client.py b/src/helm/clients/writer_client.py index 1116d2d1a2d..f3290e189e6 100644 --- a/src/helm/clients/writer_client.py +++ b/src/helm/clients/writer_client.py @@ -11,7 +11,7 @@ from writerai import Writer from writerai.types.chat_completion import ChatCompletion except ModuleNotFoundError as e: - handle_module_not_found_error(e, ["openai"]) + handle_module_not_found_error(e, ["writer"]) class WriterClient(CachingClient): From c8f6561a9e6e59374ebb19b8ff1ea537705b0f83 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 17 Mar 2026 17:07:40 -0700 Subject: [PATCH 26/89] Add model deployments for xAI (#4120) --- src/helm/benchmark/model_deployment_registry.py | 1 + .../model_deployments/xai_model_deployments.py | 10 ++++++++++ 2 files changed, 11 insertions(+) create mode 100644 src/helm/benchmark/model_deployments/xai_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index 5a033feb5ce..cd6c7718255 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -136,6 +136,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo import helm.benchmark.model_deployments.openrouter_model_deployments # noqa: F401 import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 import helm.benchmark.model_deployments.writer_model_deployments # noqa: F401 + import helm.benchmark.model_deployments.xai_model_deployments # noqa: F401 for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): if name.startswith(prefix): diff --git a/src/helm/benchmark/model_deployments/xai_model_deployments.py b/src/helm/benchmark/model_deployments/xai_model_deployments.py new file mode 100644 index 00000000000..b6fd3d7ee2f --- /dev/null +++ b/src/helm/benchmark/model_deployments/xai_model_deployments.py @@ -0,0 +1,10 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("xai") +def get_writer_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.grok_client.GrokChatClient") + ) From 579f6179ec1eed835bcb2f0da4567e186addab66 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 19 Mar 2026 09:39:24 -0700 Subject: [PATCH 27/89] Bump @types/node from 25.4.0 to 25.5.0 in /helm-frontend in the npm group (#4121) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- helm-frontend/package.json | 2 +- helm-frontend/yarn.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/helm-frontend/package.json b/helm-frontend/package.json index 50e94c1836b..d7b49e51a0c 100644 --- a/helm-frontend/package.json +++ b/helm-frontend/package.json @@ -31,7 +31,7 @@ "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^14.3.1", "@testing-library/user-event": "^14.6.1", - "@types/node": "^25.4.0", + "@types/node": "^25.5.0", "@types/react": "^18.2.15", "@types/react-dom": "^18.2.7", "@types/react-helmet": "^6.1.11", diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index 0ce437d3d80..d8e4a0d3bcb 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -1508,10 +1508,10 @@ resolved "https://registry.yarnpkg.com/@types/ms/-/ms-0.7.32.tgz#f6cd08939ae3ad886fcc92ef7f0109dacddf61ab" integrity sha512-xPSg0jm4mqgEkNhowKgZFBNtwoEwF6gJ4Dhww+GFpm3IgtNseHQZ5IqdNwnquZEoANxyDAKDRAdVo4Z72VvD/g== -"@types/node@*", "@types/node@^25.4.0": - version "25.4.0" - resolved "https://registry.yarnpkg.com/@types/node/-/node-25.4.0.tgz#f25d8467984d6667cc4c1be1e2f79593834aaedb" - integrity sha512-9wLpoeWuBlcbBpOY3XmzSTG3oscB6xjBEEtn+pYXTfhyXhIxC5FsBer2KTopBlvKEiW9l13po9fq+SJY/5lkhw== +"@types/node@*", "@types/node@^25.5.0": + version "25.5.0" + resolved "https://registry.yarnpkg.com/@types/node/-/node-25.5.0.tgz#5c99f37c443d9ccc4985866913f1ed364217da31" + integrity sha512-jp2P3tQMSxWugkCUKLRPVUpGaL5MVFwF8RDuSRztfwgN1wmqJeMSbKlnEtQqU8UrhTmzEmZdu2I6v2dpp7XIxw== dependencies: undici-types "~7.18.0" From eefc96b4dca51dd24584657fa93d1e48e5b8fafc Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 19 Mar 2026 15:55:43 -0700 Subject: [PATCH 28/89] Add Arabic legal scenario (#4122) --- .../annotation/arabic_legal_annotator.py | 94 +++++++++++++++++++ .../run_specs/arabic_enterprise_run_specs.py | 60 ++++++++++++ .../scenarios/arabic_legal_scenario.py | 85 +++++++++++++++++ .../static/schema_arabic_enterprise.yaml | 41 ++++++++ 4 files changed, 280 insertions(+) create mode 100644 src/helm/benchmark/annotation/arabic_legal_annotator.py create mode 100644 src/helm/benchmark/scenarios/arabic_legal_scenario.py diff --git a/src/helm/benchmark/annotation/arabic_legal_annotator.py b/src/helm/benchmark/annotation/arabic_legal_annotator.py new file mode 100644 index 00000000000..4cb71dd23f2 --- /dev/null +++ b/src/helm/benchmark/annotation/arabic_legal_annotator.py @@ -0,0 +1,94 @@ +import re +from typing import Any + +from helm.benchmark.adaptation.request_state import RequestState +from helm.benchmark.annotation.annotator import Annotator +from helm.clients.auto_client import AutoClient +from helm.common.request import Request +from helm.proxy.retry import NonRetriableException + + +class AnnotatorResponseParseFailure(NonRetriableException): + def __init__(self, response_text: str, **kwargs): + self.response_text = response_text + super().__init__(kwargs) + + +class ArabicLegalAnnotator(Annotator): + name = "arabic_legal" + + PROMPT_TEMPLATE = """Here is a legal question, a reference answer, and a model output, delineated with start and end tags. Judge whether the model output is equivalent to the reference answer. + + +{{question}} + + + +{{reference_answer}} + + + +{{model_output}} + + +Respond with the following format: + + +INSERT_YOUR_REASONING_HERE + + +INSERT_YOUR_SCORE_HERE + + +Please output your one-sentence concise reasoning within the "reasoning" tags and your score within the "score" tags. Your reasoning should be less than 20 tokens. The score should be either 1 or 0. Do not output any other text. +""" # noqa: E501 + + def __init__(self, auto_client: AutoClient): + self._auto_client = auto_client + + def annotate(self, request_state: RequestState) -> Any: + assert request_state.result + assert len(request_state.result.completions) == 1 + question_text = request_state.instance.input.text + reference_text = request_state.instance.references[0].output.text + prediction_text = request_state.result.completions[0].text + + annotator_prompt = ( + self.PROMPT_TEMPLATE.replace("{{question}}", question_text) + .replace("{{reference_answer}}", reference_text) + .replace("{{model_output}}", prediction_text) + ) + annotator_request = Request( + model="openai/gpt-4.1-2025-04-14", + model_deployment="openai/gpt-4.1-2025-04-14", + prompt=annotator_prompt, + temperature=0.0, + max_tokens=100, + ) + annotator_response = self._auto_client.make_request(annotator_request) + if not annotator_response.success: + raise Exception(f"Annotation request failed: {annotator_response.error}") + assert len(annotator_response.completions) == 1 + annotator_response_text = annotator_response.completions[0].text + # fuzzy match regex check, allows for different casing, or forgetting / in end tag + reasoning_match = re.search( + r"<\s*reasoning\s*>(.*?)<\/?\s*reasoning\s*>", annotator_response_text, re.DOTALL | re.IGNORECASE + ) + score_match = re.search( + r"<\s*score\s*>(.*?)<\/?\s*score\s*>", annotator_response_text, re.DOTALL | re.IGNORECASE + ) + if not reasoning_match or not score_match: + raise AnnotatorResponseParseFailure( + message=f"Could not parse markup in raw response: '{annotator_response_text}'", + response_text=annotator_response_text, + ) + reasoning = reasoning_match.group(1).strip() + try: + score = float(score_match.group(1).strip()) + except ValueError: + raise AnnotatorResponseParseFailure( + message=f"Could not parse score as float from raw request: '{annotator_response_text}'", + response_text=annotator_response_text, + ) + + return {"reasoning": reasoning, "score": score} diff --git a/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py b/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py index 0cd6cf01bb4..52f7f088290 100644 --- a/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py +++ b/src/helm/benchmark/run_specs/arabic_enterprise_run_specs.py @@ -190,3 +190,63 @@ def get_arabic_finance_calculation_spec(lang: str = "ar") -> RunSpec: annotators=annotator_specs, groups=["arabic_finance_calculation"], ) + + +@run_spec_function("arabic_legal_qa") +def get_arabic_legal_qa() -> RunSpec: + """EXPERIMENTAL: This run spec here may have future reverse incompatible changes.""" + + scenario_spec = ScenarioSpec(class_name="helm.benchmark.scenarios.arabic_legal_scenario.ArabicLegalQAScenario") + + adapter_spec = get_generation_adapter_spec( + instructions="Answer the following question briefly in Modern Standard Arabic in the context of the UAE. Respond only with the answer.", + max_tokens=1000, + stop_sequences=[], + ) + + annotator_specs = [ + AnnotatorSpec(class_name="helm.benchmark.annotation.arabic_legal_annotator.ArabicLegalAnnotator") + ] + + metric_specs = get_basic_metric_specs([]) + [ + MetricSpec(class_name="helm.benchmark.metrics.arabic_legal_metric.ArabicLegalMetric") + ] + + return RunSpec( + name="arabic_legal_qa", + scenario_spec=scenario_spec, + adapter_spec=adapter_spec, + metric_specs=metric_specs, + annotators=annotator_specs, + groups=["arabic_legal_qa"], + ) + + +@run_spec_function("arabic_legal_rag") +def get_arabic_legal_rag() -> RunSpec: + """EXPERIMENTAL: This run spec here may have future reverse incompatible changes.""" + + scenario_spec = ScenarioSpec(class_name="helm.benchmark.scenarios.arabic_legal_scenario.ArabicLegalRAGScenario") + + adapter_spec = get_generation_adapter_spec( + instructions="Answer the following question briefly in Modern Standard Arabic in the context of the UAE. Respond only with the answer.", + max_tokens=1000, + stop_sequences=[], + ) + + annotator_specs = [ + AnnotatorSpec(class_name="helm.benchmark.annotation.arabic_legal_annotator.ArabicLegalAnnotator") + ] + + metric_specs = get_basic_metric_specs([]) + [ + MetricSpec(class_name="helm.benchmark.metrics.arabic_legal_metric.ArabicLegalMetric") + ] + + return RunSpec( + name="arabic_legal_rag", + scenario_spec=scenario_spec, + adapter_spec=adapter_spec, + metric_specs=metric_specs, + annotators=annotator_specs, + groups=["arabic_legal_rag"], + ) diff --git a/src/helm/benchmark/scenarios/arabic_legal_scenario.py b/src/helm/benchmark/scenarios/arabic_legal_scenario.py new file mode 100644 index 00000000000..cf687e4c450 --- /dev/null +++ b/src/helm/benchmark/scenarios/arabic_legal_scenario.py @@ -0,0 +1,85 @@ +import os +from typing import List + +import datasets + +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Reference, + TEST_SPLIT, + CORRECT_TAG, + Input, + Output, + ScenarioMetadata, +) +from helm.common.general import ensure_directory_exists + + +class ArabicLegalScenario(Scenario): + name = "arabic_legal" + description = "Arabic Legal" + tags = ["legal"] + + TASK_NAME_QA = "qa" + TASK_NAME_RAG = "rag" + + def __init__(self, task: str): + super().__init__() + if task != self.TASK_NAME_QA and task != self.TASK_NAME_RAG: + raise ValueError( + f"Unknown task '{task}'; " f"allowed values are {self.TASK_NAME_QA} and {self.TASK_NAME_RAG}" + ) + self.task = task + + def get_instances(self, output_path: str) -> List[Instance]: + cache_dir = os.path.join(output_path, "data") + ensure_directory_exists(cache_dir) + dataset = datasets.load_dataset( + "yifanmai/arabic-enterprise", + "legal", + cache_dir=cache_dir, + split="test", + # TODO: Set revision + ) + assert isinstance(dataset, datasets.Dataset) + + instances: List[Instance] = [] + for row in dataset: + if self.task == "qa": + input = Input(text=row["question"]) + elif self.task == "rag": + input = Input(text=f"{row['context']}\n\n{row['question']}") + references = [ + Reference(Output(text=row["answer"]), tags=[CORRECT_TAG]), + ] + instances.append( + Instance( + input=input, + references=references, + split=TEST_SPLIT, + ) + ) + + return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata(name=self.name, main_metric="exact_match", main_split="test") + + +class ArabicLegalQAScenario(ArabicLegalScenario): + name = "arabic_legal_qa" + description = "Arabic Legal QA" + tags = ["legal"] + + def __init__(self): + super().__init__(task="qa") + + +class ArabicLegalRAGScenario(ArabicLegalScenario): + name = "arabic_legal_rag" + description = "Arabic Legal RAG" + tags = ["legal"] + + def __init__(self): + super().__init__(task="rag") diff --git a/src/helm/benchmark/static/schema_arabic_enterprise.yaml b/src/helm/benchmark/static/schema_arabic_enterprise.yaml index ff07df8037d..f4ff70e0ab2 100644 --- a/src/helm/benchmark/static/schema_arabic_enterprise.yaml +++ b/src/helm/benchmark/static/schema_arabic_enterprise.yaml @@ -102,6 +102,11 @@ metrics: short_display_name: Score description: Model-judged score. lower_is_better: false + - name: model_judged_score + display_name: Model Judged Score + short_display_name: Score + description: Model-judged score. + lower_is_better: false ############################################################ perturbations: [] @@ -146,6 +151,8 @@ run_groups: description: Arabic Scenarios category: Scenarios subgroups: + - arabic_legal_qa + - arabic_legal_rag - arabic_content_generation - arabic_finance_calculation - arabic_finance_mcq @@ -218,3 +225,37 @@ run_groups: who: "?" when: "2025" language: English + + - name: arabic_legal_qa + display_name: Legal QA + description: Legal QA + metric_groups: + - accuracy + - efficiency + - general_information + environment: + main_name: model_judged_score + main_split: test + taxonomy: + task: "?" + what: "?" + who: "?" + when: "2025" + language: English + + - name: arabic_legal_rag + display_name: Legal RAG + description: Legal RAG + metric_groups: + - accuracy + - efficiency + - general_information + environment: + main_name: model_judged_score + main_split: test + taxonomy: + task: "?" + what: "?" + who: "?" + when: "2025" + language: English From 7c2f30b286db47fc3ed5d7b7d2d2ec9f0891e5ff Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 19 Mar 2026 16:17:15 -0700 Subject: [PATCH 29/89] Add metric for Arabic legal scenarios (#4123) --- .../benchmark/metrics/arabic_legal_metric.py | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 src/helm/benchmark/metrics/arabic_legal_metric.py diff --git a/src/helm/benchmark/metrics/arabic_legal_metric.py b/src/helm/benchmark/metrics/arabic_legal_metric.py new file mode 100644 index 00000000000..cb3f968fa17 --- /dev/null +++ b/src/helm/benchmark/metrics/arabic_legal_metric.py @@ -0,0 +1,22 @@ +from typing import List + +from helm.benchmark.adaptation.adapter_spec import AdapterSpec +from helm.benchmark.adaptation.request_state import RequestState +from helm.benchmark.metrics.metric import Metric +from helm.benchmark.metrics.metric_name import MetricName +from helm.benchmark.metrics.metric_service import MetricService +from helm.benchmark.metrics.statistic import Stat + + +class ArabicLegalMetric(Metric): + def evaluate_generation( + self, + adapter_spec: AdapterSpec, + request_state: RequestState, + metric_service: MetricService, + eval_cache_path: str, + ) -> List[Stat]: + assert request_state.annotations + assert "arabic_legal" in request_state.annotations + score = request_state.annotations["arabic_legal"]["score"] + return [Stat(MetricName("model_judged_score")).add(score)] From 5f21a0ccbdf235b0147e2f086243d229917c3fbc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 23 Mar 2026 13:57:59 -0700 Subject: [PATCH 30/89] Bump flatted from 3.2.9 to 3.4.2 in /helm-frontend (#4124) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- helm-frontend/yarn.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index d8e4a0d3bcb..51380104e65 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -2953,9 +2953,9 @@ flat-cache@^3.0.4: rimraf "^3.0.2" flatted@^3.2.7: - version "3.2.9" - resolved "https://registry.yarnpkg.com/flatted/-/flatted-3.2.9.tgz#7eb4c67ca1ba34232ca9d2d93e9886e611ad7daf" - integrity sha512-36yxDn5H7OFZQla0/jFJmbIKTdZAQHngCedGxiMmpNfEZM0sdEeT+WczLQrjK6D7o2aiyLYDnkw0R3JK0Qv1RQ== + version "3.4.2" + resolved "https://registry.yarnpkg.com/flatted/-/flatted-3.4.2.tgz#f5c23c107f0f37de8dbdf24f13722b3b98d52726" + integrity sha512-PjDse7RzhcPkIJwy5t7KPWQSZ9cAbzQXcafsetQoD7sOJRQlGikNbx7yZp2OotDnJyrDcbyRq3Ttb18iYOqkxA== for-each@^0.3.3: version "0.3.3" From 5b08a863c9a13644782098c4f26a3217662c6328 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 23 Mar 2026 14:03:16 -0700 Subject: [PATCH 31/89] Automatically import model deployment and tokenizer config generators (#4128) --- .../benchmark/model_deployment_registry.py | 35 ++++++++++++------- src/helm/benchmark/run_spec.py | 17 ++------- .../benchmark/tokenizer_config_registry.py | 22 ++++++++++-- src/helm/common/plugins.py | 18 ++++++++++ 4 files changed, 63 insertions(+), 29 deletions(-) create mode 100644 src/helm/common/plugins.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index cd6c7718255..349c7b4e062 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -1,17 +1,19 @@ +import threading from typing import Callable, Dict, Optional, List, TypeVar from dataclasses import dataclass import cattrs import yaml -from helm.common.hierarchical_logger import hlog, hwarn -from helm.common.object_spec import ObjectSpec from helm.benchmark.model_metadata_registry import ( ModelMetadata, get_model_metadata, get_unknown_model_metadata, register_model_metadata, ) +from helm.common.hierarchical_logger import hlog, hwarn +from helm.common.object_spec import ObjectSpec +from helm.common.plugins import import_all_modules_in_package class ClientSpec(ObjectSpec): @@ -123,21 +125,28 @@ def register_model_deployments_from_path(path: str) -> None: register_model_deployment(model_deployment) +_DISCOVER_MODEL_DEPLOYMENT_GENERATORS_LOCK = threading.Lock() +_DISCOVER_MODEL_DEPLOYMENT_GENERATORS_DONE = False + + +def discover_model_deployment_generators() -> None: + """Discover and register all model deployment generators in helm.benchmark.model_deployments""" + global _DISCOVER_MODEL_DEPLOYMENT_GENERATORS_DONE + with _DISCOVER_MODEL_DEPLOYMENT_GENERATORS_LOCK: + if _DISCOVER_MODEL_DEPLOYMENT_GENERATORS_DONE: + return + _DISCOVER_MODEL_DEPLOYMENT_GENERATORS_DONE = True + + import helm.benchmark.model_deployments # noqa + + import_all_modules_in_package(helm.benchmark.model_deployments) + + def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeployment: deployment: Optional[ModelDeployment] = DEPLOYMENT_NAME_TO_MODEL_DEPLOYMENT.get(name) if not deployment: - import helm.benchmark.model_deployments.anthropic_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.huggingface_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.huggingface_inference_providers_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.litellm_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.mistralai_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.openai_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.openrouter_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.together_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.writer_model_deployments # noqa: F401 - import helm.benchmark.model_deployments.xai_model_deployments # noqa: F401 - + discover_model_deployment_generators() for prefix, model_deployment_generator in _REGISTERED_MODEL_DEPLOYMENT_GENERATORS.items(): if name.startswith(prefix): deployment = model_deployment_generator(name) diff --git a/src/helm/benchmark/run_spec.py b/src/helm/benchmark/run_spec.py index dc6600d06e4..c5879f65a6d 100644 --- a/src/helm/benchmark/run_spec.py +++ b/src/helm/benchmark/run_spec.py @@ -1,14 +1,13 @@ from dataclasses import dataclass, field -import importlib import os -import pkgutil -from typing import Callable, Dict, Iterable, List, Optional, TypeVar +from typing import Callable, Dict, List, Optional, TypeVar from helm.benchmark.adaptation.adapter_spec import AdapterSpec from helm.benchmark.augmentations.data_augmenter import DataAugmenterSpec from helm.benchmark.metrics.metric import MetricSpec from helm.benchmark.scenarios.scenario import ScenarioSpec from helm.benchmark.annotation.annotator import AnnotatorSpec +from helm.common.plugins import import_all_modules_in_package @dataclass(frozen=True) @@ -70,21 +69,11 @@ def wrap(func: F) -> F: return wrap -# Copied from https://docs.python.org/3/library/pkgutil.html#pkgutil.iter_modules -def _iter_namespace(ns_pkg) -> Iterable[pkgutil.ModuleInfo]: - # Specifying the second argument (prefix) to iter_modules makes the - # returned name an absolute name instead of a relative one. This allows - # import_module to work without having to do additional modification to - # the name. - return pkgutil.iter_modules(ns_pkg.__path__, ns_pkg.__name__ + ".") - - def discover_run_spec_functions() -> None: """Discover and register all run spec functions under helm.benchmark.run_specs""" import helm.benchmark.run_specs # noqa - for finder, name, ispkg in _iter_namespace(helm.benchmark.run_specs): - importlib.import_module(name) + import_all_modules_in_package(helm.benchmark.run_specs) def get_run_spec_function(name: str) -> Optional[RunSpecFunction]: diff --git a/src/helm/benchmark/tokenizer_config_registry.py b/src/helm/benchmark/tokenizer_config_registry.py index 9480cf516a1..fc91efceb94 100644 --- a/src/helm/benchmark/tokenizer_config_registry.py +++ b/src/helm/benchmark/tokenizer_config_registry.py @@ -1,3 +1,4 @@ +import threading from typing import Callable, Dict, Optional, List, TypeVar from dataclasses import dataclass @@ -6,6 +7,7 @@ from helm.common.hierarchical_logger import hlog from helm.common.object_spec import ObjectSpec +from helm.common.plugins import import_all_modules_in_package class TokenizerSpec(ObjectSpec): @@ -52,11 +54,27 @@ def register_tokenizer_configs_from_path(path: str) -> None: register_tokenizer_config(tokenizer_config) +_DISCOVER_TOKENIZER_CONFIG_GENERATORS_LOCK = threading.Lock() +_DISCOVER_TOKENIZER_CONFIG_GENERATORS_DONE = False + + +def discover_tokenizer_config_generators() -> None: + """Discover and register all tokenizer config generators in helm.benchmark.tokenizer_configs""" + global _DISCOVER_TOKENIZER_CONFIG_GENERATORS_DONE + with _DISCOVER_TOKENIZER_CONFIG_GENERATORS_LOCK: + if _DISCOVER_TOKENIZER_CONFIG_GENERATORS_DONE: + return + _DISCOVER_TOKENIZER_CONFIG_GENERATORS_DONE = True + + import helm.benchmark.tokenizer_configs # noqa + + import_all_modules_in_package(helm.benchmark.tokenizer_configs) + + def get_tokenizer_config(name: str) -> Optional[TokenizerConfig]: tokenizer_config: Optional[TokenizerConfig] = TOKENIZER_NAME_TO_CONFIG.get(name) if not tokenizer_config: - import helm.benchmark.tokenizer_configs.huggingface_tokenizer_configs # noqa: F401 - + discover_tokenizer_config_generators() for prefix, tokenizer_config_generator in _REGISTERED_TOKENIZER_CONFIG_GENERATORS.items(): if name.startswith(prefix): tokenizer_config = tokenizer_config_generator(name) diff --git a/src/helm/common/plugins.py b/src/helm/common/plugins.py new file mode 100644 index 00000000000..a37b48f5d3f --- /dev/null +++ b/src/helm/common/plugins.py @@ -0,0 +1,18 @@ +from types import ModuleType +from typing import Iterable +import importlib +import pkgutil + + +# Copied from https://docs.python.org/3/library/pkgutil.html#pkgutil.iter_modules +def _iter_namespace(ns_pkg) -> Iterable[pkgutil.ModuleInfo]: + # Specifying the second argument (prefix) to iter_modules makes the + # returned name an absolute name instead of a relative one. This allows + # import_module to work without having to do additional modification to + # the name. + return pkgutil.iter_modules(ns_pkg.__path__, ns_pkg.__name__ + ".") + + +def import_all_modules_in_package(module: ModuleType): + for _, name, _ in _iter_namespace(module): + importlib.import_module(name) From ffa6bee4beddc6cd4e133a70e3c697736fb513db Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 23 Mar 2026 14:03:27 -0700 Subject: [PATCH 32/89] Allow using auto-generated model deployments for models with two-part names (#4129) --- src/helm/benchmark/model_deployment_registry.py | 10 +++++++--- src/helm/benchmark/run.py | 6 ++++-- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index 349c7b4e062..a5b4203790c 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -236,9 +236,13 @@ def get_default_model_deployment_for_model( hlog("If you want to use a different model deployment, please specify it explicitly.") return chosen_deployment.name - # Some models are added but have no deployments yet. - # In this case, we return None. - return None + # See if we can auto-generate a model deployment for this model. + # Use the model deployment as the default model deployment if so. + # Otherwise, return None. + try: + return get_model_deployment(model_name).name + except ValueError: + return None ModelDeploymentGenerator = Callable[[str], ModelDeployment] diff --git a/src/helm/benchmark/run.py b/src/helm/benchmark/run.py index b7512e86424..b902d2702ea 100644 --- a/src/helm/benchmark/run.py +++ b/src/helm/benchmark/run.py @@ -24,6 +24,7 @@ from helm.benchmark.executor import ExecutionSpec from helm.benchmark.runner import Runner, RunSpec, set_benchmark_output_path from helm.benchmark.run_spec_factory import construct_run_specs +from helm.benchmark.model_deployment_registry import get_model_deployment def run_entries_to_run_specs( @@ -323,8 +324,9 @@ def helm_run(args): all_models = set(model_metadata_registry.get_all_models()) for model_to_run in args.models_to_run: if model_to_run not in all_models: - name_parts = model_to_run.split("/") - if len(name_parts) <= 2: + try: + get_model_deployment(model_to_run) + except ValueError: raise Exception(f"Unknown model '{model_to_run}' passed to --models-to-run") else: model_expander_wildcard_pattern = re.compile( From 63631d219be50f622ca8a5178581dbf8d2daf959 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 23 Mar 2026 14:03:41 -0700 Subject: [PATCH 33/89] Add Cohere model deployment generator (#4130) --- .../model_deployments/cohere_model_deployments.py | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 src/helm/benchmark/model_deployments/cohere_model_deployments.py diff --git a/src/helm/benchmark/model_deployments/cohere_model_deployments.py b/src/helm/benchmark/model_deployments/cohere_model_deployments.py new file mode 100644 index 00000000000..4e19fdac892 --- /dev/null +++ b/src/helm/benchmark/model_deployments/cohere_model_deployments.py @@ -0,0 +1,10 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("cohere") +def get_cohere_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, model_name=model_name, client_spec=ClientSpec("helm.clients.cohere_client.CohereChatClient") + ) From 4b734290474ad649934bf2648e5d0f238e1a512c Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 24 Mar 2026 15:57:51 -0700 Subject: [PATCH 34/89] Fix link for MedHELM paper (#4133) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 830d2e83a1f..9c4aff83069 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ The HELM framework was used in the following papers for evaluating models. - **Enterprise Benchmarks for Large Language Model Evaluation** - [paper](https://arxiv.org/abs/2410.12857), [documentation](https://crfm-helm.readthedocs.io/en/latest/enterprise_benchmark/) - **The Mighty ToRR: A Benchmark for Table Reasoning and Robustness** - [paper](https://arxiv.org/abs/2502.19412), [leaderboard](https://crfm.stanford.edu/helm/torr/latest/) - **Reliable and Efficient Amortized Model-based Evaluation** - [paper](https://arxiv.org/abs/2503.13335), [documentation](https://crfm-helm.readthedocs.io/en/latest/reeval/) -- **MedHELM** - paper in progress, [leaderboard](https://crfm.stanford.edu/helm/medhelm/latest/), [documentation](https://crfm-helm.readthedocs.io/en/latest/reeval/) +- **MedHELM** - [paper](https://www.nature.com/articles/s41591-025-04151-2), [leaderboard](https://crfm.stanford.edu/helm/medhelm/latest/) - **Holistic Evaluation of Audio-Language Models** - [paper](https://arxiv.org/abs/2508.21376), [leaderboard](https://crfm.stanford.edu/helm/audio/latest/) The HELM framework can be used to reproduce the published model evaluation results from these papers. To get started, refer to the documentation links above for the corresponding paper, or the [main Reproducing Leaderboards documentation](https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/). From 14092935aef76c721b1d7a95849365738d2b0023 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 24 Mar 2026 15:58:27 -0700 Subject: [PATCH 35/89] Add Mistral Small 4 (#4134) --- src/helm/config/model_deployments.yaml | 7 +++++++ src/helm/config/model_metadata.yaml | 9 +++++++++ src/helm/config/tokenizer_configs.yaml | 6 ++++++ 3 files changed, 22 insertions(+) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index 1b3f67e3f2c..4273a1979ac 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -2547,6 +2547,13 @@ model_deployments: client_spec: class_name: "helm.clients.mistral_client.MistralAIClient" + - name: mistralai/mistral-small-2603 + model_name: mistralai/mistral-small-2603 + tokenizer_name: mistralai/Mistral-Small-4-119B-2603 + max_sequence_length: 256000 + client_spec: + class_name: "helm.clients.mistral_client.MistralAIClient" + - name: mistralai/mistral-medium-2312 model_name: mistralai/mistral-medium-2312 tokenizer_name: mistralai/Mistral-7B-v0.1 diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index 3f0e3fc37df..fd861bb4a7c 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -2797,6 +2797,15 @@ models: release_date: 2025-06-20 tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: mistralai/mistral-small-2603 + display_name: Mistral Small 4 (2603) + description: Mistral Small 4 is a model in the Mistral Small family that unifies reasoning, multimodal, and agentic capabilities. ([blog](https://mistral.ai/news/mistral-small-4), [doc](https://docs.mistral.ai/models/mistral-small-4-0-26-03)) + creator_organization_name: Mistral AI + access: open + num_parameters: 119000000000 + release_date: 2026-03-16 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: mistralai/ministral-14b-2512 display_name: Ministral 3 (14B 2512) description: Ministral 3 is a family of parameter-efficient dense language models designed for compute and memory constrained applications. ([doc](https://docs.mistral.ai/models/ministral-3-14b-25-12), [blog](https://mistral.ai/news/mistral-3), [paper](https://arxiv.org/abs/2601.08584)) diff --git a/src/helm/config/tokenizer_configs.yaml b/src/helm/config/tokenizer_configs.yaml index 0e954f6d0f9..876c2a4039e 100644 --- a/src/helm/config/tokenizer_configs.yaml +++ b/src/helm/config/tokenizer_configs.yaml @@ -633,6 +633,12 @@ tokenizer_configs: end_of_text_token: "" prefix_token: "" + - name: mistralai/Mistral-Small-4-119B-2603 + tokenizer_spec: + class_name: "helm.tokenizers.huggingface_tokenizer.HuggingFaceTokenizer" + end_of_text_token: "" + prefix_token: "" + # Moonshot AI - name: moonshotai/kimi-k2-instruct tokenizer_spec: From 1433de074caa2caad3ee0b8101e6001354074257 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 24 Mar 2026 17:09:37 -0700 Subject: [PATCH 36/89] Use GPT-5.4 for annotators for Arabic Enterprise scenarios (#4136) --- .../annotation/arabic_content_generation_annotator.py | 4 ++-- .../arabic_content_generation_relative_annotator.py | 4 ++-- .../arabic_content_generation_similarity_annotator.py | 4 ++-- .../annotation/arabic_finance_calculation_annotator.py | 4 ++-- src/helm/benchmark/annotation/arabic_legal_annotator.py | 4 ++-- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py index 2ce080eca99..84f3ac7c214 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py @@ -77,8 +77,8 @@ def annotate(self, request_state: RequestState) -> Any: ) annotator_request = Request( - model="openai/gpt-4.1-nano-2025-04-14", - model_deployment="openai/gpt-4.1-nano-2025-04-14", + model="openai/gpt-5.4-2026-03-05", + model_deployment="openai/gpt-5.4-2026-03-05", prompt=annotator_prompt, temperature=0.0, max_tokens=2000, diff --git a/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py index 1e574d6caa2..46a51dc7811 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py @@ -59,8 +59,8 @@ def annotate(self, request_state: RequestState) -> Any: ) annotator_request = Request( - model="openai/gpt-5.1-2025-11-13", - model_deployment="openai/gpt-5.1-2025-11-13", + model="openai/gpt-5.4-2026-03-05", + model_deployment="openai/gpt-5.4-2026-03-05", prompt=annotator_prompt, temperature=0.0, max_tokens=2000, diff --git a/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py index 810043be325..efb21b9d7a4 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py @@ -64,8 +64,8 @@ def annotate(self, request_state: RequestState) -> Any: ) annotator_request = Request( - model="openai/gpt-5.1-2025-11-13", - model_deployment="openai/gpt-5.1-2025-11-13", + model="openai/gpt-5.4-2026-03-05", + model_deployment="openai/gpt-5.4-2026-03-05", prompt=annotator_prompt, temperature=0.0, max_tokens=2000, diff --git a/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py b/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py index 45160bc8b4e..8f35f950fdf 100644 --- a/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_finance_calculation_annotator.py @@ -59,8 +59,8 @@ def annotate(self, request_state: RequestState) -> Any: .replace("{{model_output}}", prediction_text) ) annotator_request = Request( - model="openai/gpt-4.1-nano-2025-04-14", - model_deployment="openai/gpt-4.1-nano-2025-04-14", + model="openai/gpt-5.4-2026-03-05", + model_deployment="openai/gpt-5.4-2026-03-05", prompt=annotator_prompt, temperature=0.0, max_tokens=100, diff --git a/src/helm/benchmark/annotation/arabic_legal_annotator.py b/src/helm/benchmark/annotation/arabic_legal_annotator.py index 4cb71dd23f2..c3955cee908 100644 --- a/src/helm/benchmark/annotation/arabic_legal_annotator.py +++ b/src/helm/benchmark/annotation/arabic_legal_annotator.py @@ -59,8 +59,8 @@ def annotate(self, request_state: RequestState) -> Any: .replace("{{model_output}}", prediction_text) ) annotator_request = Request( - model="openai/gpt-4.1-2025-04-14", - model_deployment="openai/gpt-4.1-2025-04-14", + model="openai/gpt-5.4-2026-03-05", + model_deployment="openai/gpt-5.4-2026-03-05", prompt=annotator_prompt, temperature=0.0, max_tokens=100, From 0135737e6fa8dd10c15032f2e1cbded5be0115f4 Mon Sep 17 00:00:00 2001 From: Ahmed Abulkhair <31564865+aaabulkhair@users.noreply.github.com> Date: Wed, 25 Mar 2026 02:11:36 +0200 Subject: [PATCH 37/89] Fix MadinahQA scenario to include Context field for reading comprehension (#4127) --- src/helm/benchmark/scenarios/madinah_qa_scenario.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/helm/benchmark/scenarios/madinah_qa_scenario.py b/src/helm/benchmark/scenarios/madinah_qa_scenario.py index 1423e12cad2..d0b3d179374 100644 --- a/src/helm/benchmark/scenarios/madinah_qa_scenario.py +++ b/src/helm/benchmark/scenarios/madinah_qa_scenario.py @@ -49,7 +49,16 @@ def get_instances(self, output_path: str) -> List[Instance]: for split_name, dataset in dataset_splits.items(): assert isinstance(dataset, datasets.Dataset) for row_index, row in enumerate(dataset): - input = Input(text=row["Question"]) + # Include the Context field (reading passage) when available. + # The General subset has a Context passage for 602/612 questions; + # the Grammar subset has no Context (all null). + # This matches the original ArabicMMLU evaluation code and LightEval. + context = row.get("Context") + question = row["Question"] + if context and isinstance(context, str) and context.strip(): + input = Input(text=f"{context}\n\n{question}") + else: + input = Input(text=question) references: List[Reference] = [] correct_option_index = ord(row["Answer Key"]) - ord("A") + 1 for option_index in range(1, 6): From 2fd9c305bf6bbcdcd0e86fb5069e185de29f0e41 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 24 Mar 2026 19:53:49 -0700 Subject: [PATCH 38/89] Update documentation for Efficient Benchmarking paper (#4132) --- README.md | 1 + ...get_helm_rank.md => efficient_benchmarking.md} | 15 +++++---------- mkdocs.yml | 2 +- 3 files changed, 7 insertions(+), 11 deletions(-) rename docs/{get_helm_rank.md => efficient_benchmarking.md} (82%) diff --git a/README.md b/README.md index 9c4aff83069..5f6c9f159f8 100644 --- a/README.md +++ b/README.md @@ -81,6 +81,7 @@ The HELM framework was used in the following papers for evaluating models. - **Image2Struct: Benchmarking Structure Extraction for Vision-Language Models** - [paper](https://arxiv.org/abs/2410.22456) - **Enterprise Benchmarks for Large Language Model Evaluation** - [paper](https://arxiv.org/abs/2410.12857), [documentation](https://crfm-helm.readthedocs.io/en/latest/enterprise_benchmark/) - **The Mighty ToRR: A Benchmark for Table Reasoning and Robustness** - [paper](https://arxiv.org/abs/2502.19412), [leaderboard](https://crfm.stanford.edu/helm/torr/latest/) +- **Efficient Benchmarking of Language Models** - [paper](https://arxiv.org/abs/2308.11696), [documentation](https://crfm-helm.readthedocs.io/en/latest/efficient_benchmarking/) - **Reliable and Efficient Amortized Model-based Evaluation** - [paper](https://arxiv.org/abs/2503.13335), [documentation](https://crfm-helm.readthedocs.io/en/latest/reeval/) - **MedHELM** - [paper](https://www.nature.com/articles/s41591-025-04151-2), [leaderboard](https://crfm.stanford.edu/helm/medhelm/latest/) - **Holistic Evaluation of Audio-Language Models** - [paper](https://arxiv.org/abs/2508.21376), [leaderboard](https://crfm.stanford.edu/helm/audio/latest/) diff --git a/docs/get_helm_rank.md b/docs/efficient_benchmarking.md similarity index 82% rename from docs/get_helm_rank.md rename to docs/efficient_benchmarking.md index 1627885bb3a..daf64c2be0c 100644 --- a/docs/get_helm_rank.md +++ b/docs/efficient_benchmarking.md @@ -1,22 +1,17 @@ -# Efficient-HELM +# Efficient Benchmarking This tutorial will show you how to locally add your model into the HELM Classic leaderboard at a fraction of the cost of performing a full run, using a techinque from IBM Resarch described in [Efficient Benchmarking (of Language Models)](https://arxiv.org/pdf/2308.11696.pdf) a paper from IBM Research. -**Warning** — The tutorial will currently only work for the HELM Classic leaderboard. Other leaderboards are not yet supported. +**Warning** — The tutorial will only work for version v0.3.0 of the HELM Classic leaderboard. Other leaderboards and versions are not supported. ## Download HELM leaderboard results -First, in order to compare your model to the latest and greatest models found in the [HELM Classic leaderboard](https://crfm.stanford.edu/helm/classic/latest/?group=core_scenarios), use the following command to obtain a zip file of all previous HELM Classic results +First, in order to compare your model to the models found in the [HELM Classic leaderboard](https://crfm.stanford.edu/helm/classic/latest/?group=core_scenarios), use the following commands to download and unzip a zip file of previous HELM Classic results: ```bash export LEADERBOARD_VERSION=v0.3.0 -``` - -Downloaded, expand the file into HELMs results dir: - -```bash -curl -O https://storage.googleapis.com/crfm-helm-public/benchmark_output/archives/$LEADERBOARD_VERSION/run_stats.zip &&\ -mkdir -p benchmark_output/runs/$LEADERBOARD_VERSION && unzip run_stats.zip -d benchmark_output/runs/$LEADERBOARD_VERSION +curl -O https://storage.googleapis.com/crfm-helm-public/efficient_helm/classic_${LEADERBOARD_VERSION}_run_stats.zip &&\ +mkdir -p benchmark_output/runs/$LEADERBOARD_VERSION && unzip classic_${LEADERBOARD_VERSION}_run_stats.zip -d benchmark_output/runs/$LEADERBOARD_VERSION ``` Now that the files are in your results directory, all HELM models will be shown in your UI along with your model. diff --git a/mkdocs.yml b/mkdocs.yml index 6f34a9ee96e..c9f44c10ef8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -48,13 +48,13 @@ nav: - adding_new_tokenizers.md - downloading_raw_results.md - reproducing_leaderboards.md - - get_helm_rank.md - benchmark.md - huggingface_models.md - Papers: - heim.md - vhelm.md - enterprise_benchmark.md + - efficent_benchmarking.md - reeval.md - medhelm.md - Reference: From db129097aa3a09097c0c057c8214c2ca42c86eb4 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 26 Mar 2026 10:57:23 -0700 Subject: [PATCH 39/89] Add model deployments for Google Vertex AI and Gemini API (#4131) --- .../benchmark/model_deployment_registry.py | 6 +- .../google_model_deployments.py | 58 +++++++++++++++++++ src/helm/benchmark/run.py | 4 +- 3 files changed, 65 insertions(+), 3 deletions(-) create mode 100644 src/helm/benchmark/model_deployments/google_model_deployments.py diff --git a/src/helm/benchmark/model_deployment_registry.py b/src/helm/benchmark/model_deployment_registry.py index a5b4203790c..366ba4224d7 100644 --- a/src/helm/benchmark/model_deployment_registry.py +++ b/src/helm/benchmark/model_deployment_registry.py @@ -24,6 +24,10 @@ class WindowServiceSpec(ObjectSpec): pass +class ModelDeploymentNotFoundError(ValueError): + pass + + @dataclass(frozen=True) class ModelDeployment: """ @@ -152,7 +156,7 @@ def get_model_deployment(name: str, warn_deprecated: bool = False) -> ModelDeplo deployment = model_deployment_generator(name) if not deployment: - raise ValueError(f"Model deployment {name} not found") + raise ModelDeploymentNotFoundError(f"Model deployment {name} not found") if deployment.deprecated and warn_deprecated: hwarn(f"Model deployment {name} is deprecated") diff --git a/src/helm/benchmark/model_deployments/google_model_deployments.py b/src/helm/benchmark/model_deployments/google_model_deployments.py new file mode 100644 index 00000000000..fe35044a682 --- /dev/null +++ b/src/helm/benchmark/model_deployments/google_model_deployments.py @@ -0,0 +1,58 @@ +from helm.benchmark.model_deployment_registry import ClientSpec, ModelDeployment, model_deployment_generator + + +@model_deployment_generator("google") +def get_google_model_deployment(name: str): + name_parts = name.split("/") + assert len(name_parts) >= 2 + model_name = "/".join(name_parts[-2:]) + genai_model_name = name_parts[-1] if name_parts[-2] == "google" else model_name + + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec("helm.clients.google_genai_client.GoogleGenAIClient", {"genai_model": genai_model_name}), + ) + + +@model_deployment_generator("gemini") +def get_gemini_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + assert len(name_parts) >= 2 + if len(name_parts) < 3 or name_parts[-2] != "google": + raise ValueError("Model name must be in the format gemini/google/model") + genai_model_name = name_parts[-1] + + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.google_genai_client.GoogleGenAIClient", + { + "genai_model": genai_model_name, + "genai_use_vertexai": False, + }, + ), + ) + + +@model_deployment_generator("vertexai") +def get_vertexai_model_deployment(name: str): + name_parts = name.split("/") + model_name = "/".join(name_parts[-2:]) + assert len(name_parts) >= 2 + if len(name_parts) < 3: + raise ValueError("Model name must be in the format vertexai/publisher/model") + genai_model_name = name_parts[-1] if name_parts[-2] == "google" else "/".join(name_parts[-2:]) + return ModelDeployment( + name=name, + model_name=model_name, + client_spec=ClientSpec( + "helm.clients.google_genai_client.GoogleGenAIClient", + { + "genai_model": genai_model_name, + "genai_use_vertexai": True, + }, + ), + ) diff --git a/src/helm/benchmark/run.py b/src/helm/benchmark/run.py index b902d2702ea..406130346d8 100644 --- a/src/helm/benchmark/run.py +++ b/src/helm/benchmark/run.py @@ -24,7 +24,7 @@ from helm.benchmark.executor import ExecutionSpec from helm.benchmark.runner import Runner, RunSpec, set_benchmark_output_path from helm.benchmark.run_spec_factory import construct_run_specs -from helm.benchmark.model_deployment_registry import get_model_deployment +from helm.benchmark.model_deployment_registry import ModelDeploymentNotFoundError, get_model_deployment def run_entries_to_run_specs( @@ -326,7 +326,7 @@ def helm_run(args): if model_to_run not in all_models: try: get_model_deployment(model_to_run) - except ValueError: + except ModelDeploymentNotFoundError: raise Exception(f"Unknown model '{model_to_run}' passed to --models-to-run") else: model_expander_wildcard_pattern = re.compile( From d97df41b2468abdff44ac2641fd60d8012790872 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 26 Mar 2026 10:59:39 -0700 Subject: [PATCH 40/89] Bump cryptography from 46.0.5 to 46.0.6 (#4137) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d2a1be49a60..aa67506da7b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,7 @@ colorcet==3.1.0 colorlog==6.10.1 confection==0.1.5 contourpy==1.3.2 -cryptography==46.0.5 +cryptography==46.0.6 cycler==0.12.1 cymem==2.0.13 dacite==1.9.2 From 13650f7057293dad7be91c8d3b9336cd7498c400 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 26 Mar 2026 10:59:49 -0700 Subject: [PATCH 41/89] Bump requests from 2.32.5 to 2.33.0 (#4138) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index aa67506da7b..529a082c1f3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -127,7 +127,7 @@ python-dateutil==2.9.0.post0 pytz==2025.2 pyyaml==6.0.3 regex==2026.1.15 -requests==2.32.5 +requests==2.33.0 retrying==1.4.2 rich==13.9.4 rouge-score==0.1.2 From c158ab9f899d5393729422ac2778543db7bfb937 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 26 Mar 2026 11:11:20 -0700 Subject: [PATCH 42/89] Update MedHELM paper title (#4139) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5f6c9f159f8..eda7cdd3aae 100644 --- a/README.md +++ b/README.md @@ -83,7 +83,7 @@ The HELM framework was used in the following papers for evaluating models. - **The Mighty ToRR: A Benchmark for Table Reasoning and Robustness** - [paper](https://arxiv.org/abs/2502.19412), [leaderboard](https://crfm.stanford.edu/helm/torr/latest/) - **Efficient Benchmarking of Language Models** - [paper](https://arxiv.org/abs/2308.11696), [documentation](https://crfm-helm.readthedocs.io/en/latest/efficient_benchmarking/) - **Reliable and Efficient Amortized Model-based Evaluation** - [paper](https://arxiv.org/abs/2503.13335), [documentation](https://crfm-helm.readthedocs.io/en/latest/reeval/) -- **MedHELM** - [paper](https://www.nature.com/articles/s41591-025-04151-2), [leaderboard](https://crfm.stanford.edu/helm/medhelm/latest/) +- **Holistic evaluation of large language models for medical tasks with MedHELM** - [paper](https://www.nature.com/articles/s41591-025-04151-2), [leaderboard](https://crfm.stanford.edu/helm/medhelm/latest/) - **Holistic Evaluation of Audio-Language Models** - [paper](https://arxiv.org/abs/2508.21376), [leaderboard](https://crfm.stanford.edu/helm/audio/latest/) The HELM framework can be used to reproduce the published model evaluation results from these papers. To get started, refer to the documentation links above for the corresponding paper, or the [main Reproducing Leaderboards documentation](https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/). From 96eadb21c38ad95d3d8ee0e8e1875bb7e7f35831 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 26 Mar 2026 11:46:19 -0700 Subject: [PATCH 43/89] Install dependencies for Read the Docs with uv (#4140) --- .readthedocs.yaml | 15 ++- docs/editing_documentation.md | 8 +- docs/requirements.txt | 5 - pyproject.toml | 8 ++ uv.lock | 237 ++++++++++++++++++++++++++++++++++ 5 files changed, 258 insertions(+), 15 deletions(-) delete mode 100644 docs/requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 90eb4aedbcb..f8a8f2442f3 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -6,9 +6,12 @@ build: os: "ubuntu-20.04" tools: python: "3.10" - -python: - install: - - requirements: docs/requirements.txt - - method: pip - path: . + jobs: + pre_create_environment: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + create_environment: + - uv venv "${READTHEDOCS_VIRTUALENV_PATH}" + install: + - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --frozen --group docs diff --git a/docs/editing_documentation.md b/docs/editing_documentation.md index 571bc9d77a5..7a234ff4fd7 100644 --- a/docs/editing_documentation.md +++ b/docs/editing_documentation.md @@ -4,16 +4,16 @@ The documentation that you are reading now is an invaluable resource for newcome We currently use the [MkDocs](https://www.mkdocs.org/) as our static site generator and [ReadTheDocs](https://readthedocs.org/) as our web host. -To edit the documentation, first clone the repository locally, then install HELM from the repository by following the [Developer Setup](developer_setup.md) instructions. After that, install the MkDocs dependencies by running the following from the root of the repository: +To edit the documentation, first clone the repository locally, then use uv to install HELM's docs dependency group: ```sh -pip install -r docs/requirements.txt +uv sync --group docs ``` -You should now be able to run MkDocs from the root of the repository: +You should now be able to run MkDocs using uv: ```sh -mkdocs serve +uv run mkdocs serve ``` Then navigate to [http://localhost:8000/](http://localhost:8000/) to view your locally-built documentation. diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 70f930c2aa2..00000000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -mkdocs==1.6.0 -mkdocstrings==0.30.1 -mkdocstrings-python==1.3.0 -mkdocs-macros-plugin==1.5.0 -mkdocs-include-markdown-plugin==4.0.0 diff --git a/pyproject.toml b/pyproject.toml index 26f35f12f85..078966f29cb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ 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2001 From: Yifan Mai Date: Thu, 26 Mar 2026 13:35:51 -0700 Subject: [PATCH 44/89] Remove requirements.txt and install-dev.sh (#4142) --- ...-requirements.yml => update-dependencies.yml} | 16 +++++++--------- ...requirements.yml => upgrade-dependencies.yml} | 14 ++++++-------- install-dev.sh | 16 ---------------- 3 files changed, 13 insertions(+), 33 deletions(-) rename .github/workflows/{update-requirements.yml => update-dependencies.yml} (84%) rename .github/workflows/{upgrade-requirements.yml => upgrade-dependencies.yml} (85%) delete mode 100755 install-dev.sh diff --git a/.github/workflows/update-requirements.yml b/.github/workflows/update-dependencies.yml similarity index 84% rename from .github/workflows/update-requirements.yml rename to .github/workflows/update-dependencies.yml index c97076530f6..bcd20217567 100644 --- a/.github/workflows/update-requirements.yml +++ b/.github/workflows/update-dependencies.yml @@ -2,7 +2,7 @@ # for all Python dependencies (including transitive dependencies) # whenever pyproject.toml is modified or the workflow is manually triggered. -name: Update requirements +name: Update dependencies on: push: @@ -11,12 +11,12 @@ on: paths: - "pyproject.toml" - "constraints.txt" - - '.github/workflows/update-requirements.yml' + - '.github/workflows/update-dependencies.yml' workflow_dispatch: jobs: - update-requirements: - name: Update requirements + update-dependencies: + name: Update dependencies runs-on: ubuntu-latest strategy: matrix: @@ -36,8 +36,6 @@ jobs: run: uv lock - name: Install dependencies run: uv sync --extra ci - - name: Write requirements.txt - run: uv pip freeze --exclude-editable > requirements.txt # Need to manually run tests here because the pull request opened later will not # run the test workflow. # @@ -57,8 +55,8 @@ jobs: - name: Create pull request uses: peter-evans/create-pull-request@v6 with: - commit-message: Update requirements - title: "Update requirements" - branch: actions/update-requirements + commit-message: Update dependencies + title: "Update dependencies" + branch: actions/update-dependencies delete-branch: true body: Auto-generated from GitHub Actions. diff --git a/.github/workflows/upgrade-requirements.yml b/.github/workflows/upgrade-dependencies.yml similarity index 85% rename from .github/workflows/upgrade-requirements.yml rename to .github/workflows/upgrade-dependencies.yml index 664e2b01f7c..f04e00e4d04 100644 --- a/.github/workflows/upgrade-requirements.yml +++ b/.github/workflows/upgrade-dependencies.yml @@ -2,7 +2,7 @@ # for all Python dependencies (including transitive dependencies) # every Monday or whenever the workflow is manually triggered. -name: Upgrade requirements +name: Upgrade dependencies on: # schedule: @@ -10,8 +10,8 @@ on: workflow_dispatch: jobs: - upgrade-requirements: - name: Upgrade requirements + upgrade-dependencies: + name: Upgrade dependencies runs-on: ubuntu-latest strategy: matrix: @@ -31,8 +31,6 @@ jobs: run: uv lock --upgrade - name: Install dependencies run: uv sync --extra ci - - name: Write requirements.txt - run: uv pip freeze --exclude-editable > requirements.txt # Need to manually run tests here because the pull request opened later will not # run the test workflow. # @@ -52,8 +50,8 @@ jobs: - name: Create pull request uses: peter-evans/create-pull-request@v6 with: - commit-message: Upgrade requirements - title: "Upgrade requirements" - branch: actions/upgrade-requirements + commit-message: Upgrade dependencies + title: "Upgrade dependencies" + branch: actions/upgrade-dependencies delete-branch: true body: Auto-generated from GitHub Actions. diff --git a/install-dev.sh b/install-dev.sh deleted file mode 100755 index da77ef58bd1..00000000000 --- a/install-dev.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -# This script installs pinned dependencies, then install CRFM-HELM in edit mode - -set -e - -pip install --upgrade setuptools -# On Mac OS, skip installing pytorch with CUDA because CUDA is not supported -if [[ $OSTYPE != 'darwin'* ]]; then - # Manually install pytorch with `--no-cache-dir` to avoid pip getting killed: https://stackoverflow.com/a/54329850 - pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121 --no-cache-dir -fi -# Install all pinned dependencies -pip install -r requirements.txt -# Install HELM in edit mode -pip install -e .[all,dev] From 781c08b6f0a13d506ee4a37fa917f300794b110d Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 26 Mar 2026 17:11:18 -0700 Subject: [PATCH 45/89] Update changelog (#4135) --- CHANGELOG.md | 44 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b58970dca60..f5e870fa2d7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,50 @@ ## [Upcoming] +### Breaking Changes + +- Previously, responses from models on the OpenAI, Anthropic Claude, OpenRouter, NVIDIA NIM, vLLM, and xAI Grok APIs were tokenized locally locally. These responses will no longer be tokenized. (#4108, #4113) +- The MadinahQA scenario will now include the context field in the inputs for the reading comprehension task (#4127) + +### Models + +- Add Mistral Large 3, Mistral Medium 3.1, Mistral Small 3.2, and Ministral 3 (#4098) +- Add GPT-5.4, GPT-5.4 mini, and GPT-5.4 nano (#4099) +- Switch llama-4-scout-17b-16e-instruct to use Vertex AI instead of Together (#4104) +- Add the ability to use models from certain providers without manually configuring `model_deployments.yaml` (#4100, #4103, #4117): + - The following providers are supported: + - OpenAI (#4107) + - Anthropic (#4109) + - OpenRouter (#4114) + - Mistral (#4118) + - Writer (#4119) + - xAI (#4120) + - Cohere (#4130) + - Google (#4131) + - Hugging Face Hub (from v0.5.12) + - HuggingFace Inference Providers (from v0.5.13) + - Together (from v0.5.12) + - LiteLLM (from v0.5.12) + - Note: Together models will now use the chat API rather than the text completions API by default (#4106) +- Remove tokenization from `AnthropicMessagesClient` (#4108) +- Remove tokenization from `OpenAIClient` (#4113) + +### Scenarios + +- Fix MadinahQA scenario to include Context field for reading comprehension (#4127) + +### Framework + +- Remove the human-evaluation optional dependency (#4101) +- Install dependencies for documentation on Read the Docs with uv (#4140) + +### Contributors + +Thank you to the following contributors for your work on this HELM release! + +- @aaabulkhair +- @yifanmai + ## [v0.5.13] - 2026-02-25 ### Breaking Changes From b0739ed419ecc2c73919287b8f693fe654c78ccc Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 26 Mar 2026 17:22:13 -0700 Subject: [PATCH 46/89] Release v0.5.14 (#4143) --- CHANGELOG.md | 5 ++++- pyproject.toml | 2 +- uv.lock | 2 +- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f5e870fa2d7..239534dd456 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,8 @@ ## [Upcoming] +## [v0.5.14] - 2026-03-26 + ### Breaking Changes - Previously, responses from models on the OpenAI, Anthropic Claude, OpenRouter, NVIDIA NIM, vLLM, and xAI Grok APIs were tokenized locally locally. These responses will no longer be tokenized. (#4108, #4113) @@ -1367,7 +1369,8 @@ Thank you to the following contributors for your contributions to this HELM rele - Initial release -[upcoming]: https://github.com/stanford-crfm/helm/compare/v0.5.13...HEAD +[upcoming]: https://github.com/stanford-crfm/helm/compare/v0.5.14...HEAD +[v0.5.14]: https://github.com/stanford-crfm/helm/releases/tag/v0.5.14 [v0.5.13]: https://github.com/stanford-crfm/helm/releases/tag/v0.5.13 [v0.5.12]: https://github.com/stanford-crfm/helm/releases/tag/v0.5.12 [v0.5.11]: https://github.com/stanford-crfm/helm/releases/tag/v0.5.11 diff --git a/pyproject.toml b/pyproject.toml index 078966f29cb..81c48c0510f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "crfm-helm" -version = "0.5.13" +version = "0.5.14" authors = [ { name = "Stanford CRFM", email = "contact-crfm@stanford.edu" } ] diff --git a/uv.lock b/uv.lock index 2594e295e9f..e372dab32a0 100644 --- a/uv.lock +++ b/uv.lock @@ -1290,7 +1290,7 @@ wheels = [ [[package]] name = "crfm-helm" -version = "0.5.13" +version = "0.5.14" source = { editable = "." } dependencies = [ { name = "bottle" }, From 99be551f7d4d85213b7bf2110b9d041d8990ab41 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 30 Mar 2026 17:50:33 -0700 Subject: [PATCH 47/89] Bump requests from 2.33.0 to 2.33.1 (#4146) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 529a082c1f3..82862e4c0ee 100644 --- a/requirements.txt +++ b/requirements.txt @@ -127,7 +127,7 @@ python-dateutil==2.9.0.post0 pytz==2025.2 pyyaml==6.0.3 regex==2026.1.15 -requests==2.33.0 +requests==2.33.1 retrying==1.4.2 rich==13.9.4 rouge-score==0.1.2 From 87131cf399b7927ed61b8c532d86fb236256adff Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 30 Mar 2026 17:51:21 -0700 Subject: [PATCH 48/89] Add GPT-5.4 mini and nano (#4145) --- src/helm/config/model_deployments.yaml | 14 ++++++++++++++ src/helm/config/model_metadata.yaml | 16 ++++++++++++++++ 2 files changed, 30 insertions(+) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index 4273a1979ac..a661a8f175e 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -2890,6 +2890,20 @@ model_deployments: client_spec: class_name: "helm.clients.openai_responses_client.OpenAIResponseClient" + - name: openai/gpt-5.4-mini-2026-03-17 + model_name: openai/gpt-5.4-mini-2026-03-17 + tokenizer_name: openai/o200k_base + max_sequence_length: 400000 + client_spec: + class_name: "helm.clients.openai_responses_client.OpenAIResponseClient" + + - name: openai/gpt-5.4-nano-2026-03-17 + model_name: openai/gpt-5.4-nano-2026-03-17 + tokenizer_name: openai/o200k_base + max_sequence_length: 400000 + client_spec: + class_name: "helm.clients.openai_responses_client.OpenAIResponseClient" + - name: openai/gpt-5.4-2026-03-05 model_name: openai/gpt-5.4-2026-03-05 tokenizer_name: openai/o200k_base diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index fd861bb4a7c..d78ad2c2483 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -3343,6 +3343,22 @@ models: release_date: 2026-03-05 tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, OPENAI_CHATGPT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: openai/gpt-5.4-mini-2026-03-17 + display_name: GPT-5.4 mini (2026-03-17) + description: GPT-5.4 mini (2026-03-17) is a more efficient model designed for high-volume workloads. ([blog](https://openai.com/index/introducing-gpt-5-4-mini-and-nano/)) + creator_organization_name: OpenAI + access: limited + release_date: 2026-03-17 + tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, OPENAI_CHATGPT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: openai/gpt-5.4-nano-2026-03-17 + display_name: GPT-5.4 nano (2026-03-17) + description: GPT-5.4 nano (2026-03-17) is a more efficient model designed for high-volume workloads. ([blog](https://openai.com/index/introducing-gpt-5-4-mini-and-nano/)) + creator_organization_name: OpenAI + access: limited + release_date: 2026-03-17 + tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, OPENAI_CHATGPT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: openai/gpt-5.2-2025-12-11 display_name: GPT-5.2 (2025-12-11) description: GPT-5.2 (2025-12-11) is a model in the GPT-5 model family that is intended for coding and agentic tasks across industries. ([blog](https://openai.com/index/introducing-gpt-5-2/)) From d53b084910b09f64591c595a249de13c3c884fe6 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 30 Mar 2026 17:51:45 -0700 Subject: [PATCH 49/89] Minor fix to changelog (#4144) --- CHANGELOG.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 239534dd456..0bb3baea346 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,10 @@ ## [Upcoming] +### Models + +- Add GPT-5.4, GPT-5.4 mini, and GPT-5.4 nano (#4145) + ## [v0.5.14] - 2026-03-26 ### Breaking Changes @@ -12,7 +16,7 @@ ### Models - Add Mistral Large 3, Mistral Medium 3.1, Mistral Small 3.2, and Ministral 3 (#4098) -- Add GPT-5.4, GPT-5.4 mini, and GPT-5.4 nano (#4099) +- Add GPT-5.4 (#4099) - Switch llama-4-scout-17b-16e-instruct to use Vertex AI instead of Together (#4104) - Add the ability to use models from certain providers without manually configuring `model_deployments.yaml` (#4100, #4103, #4117): - The following providers are supported: From ff9862654187a99e46b0b9732755a1c71d6ffc41 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 31 Mar 2026 16:05:54 -0700 Subject: [PATCH 50/89] Add HELM Arabic Enterprise landing (#4147) --- .../Landing/ArabicEnterpriseLanding.tsx | 112 ++++++++++++++++++ helm-frontend/src/routes/Home.tsx | 3 + 2 files changed, 115 insertions(+) create mode 100644 helm-frontend/src/components/Landing/ArabicEnterpriseLanding.tsx diff --git a/helm-frontend/src/components/Landing/ArabicEnterpriseLanding.tsx b/helm-frontend/src/components/Landing/ArabicEnterpriseLanding.tsx new file mode 100644 index 00000000000..d7277d546a9 --- /dev/null +++ b/helm-frontend/src/components/Landing/ArabicEnterpriseLanding.tsx @@ -0,0 +1,112 @@ +import MiniLeaderboard from "@/components/MiniLeaderboard"; +import { Link } from "react-router-dom"; + +export default function ArabicEnterpriseLanding() { + return ( +
+

HELM Arabic

+
+
+

+ We present{" "} + HELM Arabic Enterpise, a + leaderboard for transparent and reproducible evaluation of LLMs on + Arabic language benchmarks for enterprise use cases. This + leaderboard was created in collaboration with{" "} + + Arabic.AI + + . +

+ +

+ HELM Arabic Enterprise introduces new datasets for the following + tasks: +

+ +
    +
  • + Content Generation — + Given summaries of from real news articles, the LLM is prompted to + generate new articles in a corporate style. The generated articles + are then graded by an LLM-as-judge for faithfulness, completeness, + and style adherence. +
    • +
  • + Financial — Given + questions from English language finance textbooks that have been + translated to Arabic using machine translation, the LLM is + prompted to answer the questions in one of the following three + settings. In the MCQ setting, the LLM must choose between three + possible choices. In the boolean setting, the LLM must decide if a + given statement is true or false. In the calculation setting, the + LLM must perform mathematical calculations to produce a numeric + answer. +
    • +
  • + Legal — Given + open-ended questions written by Arabic legal experts, the LLM is + prompted to respond with short answers to the questions in one of + the following two settings. In the RAG setting, the LLM is also + provided with the relevant legal statute in the prompt. In the QA + setting, the LLM is not provided with the relevant legal statute, + and must answer relying only on its domain knowledge. +
    • +
+ +

+ The{" "} + + leaderboard results + {" "} + show a wide range of scores on these tasks. As with all HELM + leaderboards, this leaderboard provides full transparency into all + LLM requests and responses, and the results are{" "} + + reproducible + {" "} + using the{" "} + + HELM open source framework + + . We hope that this leaderboard will be a valuable resource for the + Arabic NLP community. +

+ + +
+
+ +
+ + + +
+
+
+
+ ); +} diff --git a/helm-frontend/src/routes/Home.tsx b/helm-frontend/src/routes/Home.tsx index 144ac5258dc..edcd5f16f2d 100644 --- a/helm-frontend/src/routes/Home.tsx +++ b/helm-frontend/src/routes/Home.tsx @@ -24,6 +24,7 @@ import ViLLMLanding from "@/components/Landing/ViLLMLanding"; import SLPHelmLanding from "@/components/Landing/SLPHelmLanding"; import AudioLanding from "@/components/Landing/AudioLanding"; import ArabicLanding from "@/components/Landing/ArabicLanding"; +import ArabicEnterpriseLanding from "@/components/Landing/ArabicEnterpriseLanding"; import RoboRewardLanding from "@/components/Landing/RoboRewardLanding"; export default function Home() { @@ -80,6 +81,8 @@ export default function Home() { return ; } else if (window.PROJECT_ID === "robo-reward-bench") { return ; + } else if (window.PROJECT_ID === "arabic-enterprise") { + return ; } else if (window.PROJECT_ID === "home") { return ; } else { From 0b373b7ae9cb887560c9ce4f22ea169affec5ff7 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 1 Apr 2026 12:30:56 -0700 Subject: [PATCH 51/89] Suppress duplicate warnings from truncate_sequence (#4151) --- src/helm/clients/client.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/src/helm/clients/client.py b/src/helm/clients/client.py index 506bd719e94..e7884ebd3ed 100644 --- a/src/helm/clients/client.py +++ b/src/helm/clients/client.py @@ -1,6 +1,6 @@ import json from abc import ABC, abstractmethod -from typing import List, Mapping, Optional, cast +from typing import List, Mapping, Optional, Set, Tuple, cast from helm.common.hierarchical_logger import hwarn from helm.common.media_object import MultimediaObject, TEXT_TYPE @@ -44,6 +44,10 @@ def make_cache_key(raw_request: Mapping, request: Request) -> Mapping: return {**raw_request} +_truncate_sequence_needs_to_strip_warnings: Set[Tuple[str, str]] = set() +"""Set of (model, stop) tuples that have been warned about, for suppressing repeated warnings""" + + def truncate_sequence( sequence: GeneratedOutput, request: Request, @@ -99,7 +103,13 @@ def truncate_sequence( new_logprob = sum(token.logprob for token in new_tokens) if print_warning: - hwarn(f"truncate_sequence needs to strip {json.dumps(stop)}") + warning_key = (request.model, stop) + if warning_key not in _truncate_sequence_needs_to_strip_warnings: + _truncate_sequence_needs_to_strip_warnings.add(warning_key) + hwarn( + f"truncate_sequence needs to strip {json.dumps(stop)} " + "(subsequent duplicate warnings will be suppressed)" + ) sequence = GeneratedOutput(text=new_text, logprob=new_logprob, tokens=new_tokens) From 7234e6466d25493338c723fd5c7206583119e3bd Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 1 Apr 2026 12:31:29 -0700 Subject: [PATCH 52/89] Add Llama 4 Maverick on Vertex AI Llama 4 API Service (#4150) --- src/helm/config/model_deployments.yaml | 20 ++++++++++++++++---- src/helm/config/model_metadata.yaml | 9 +++++++++ 2 files changed, 25 insertions(+), 4 deletions(-) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index a661a8f175e..d1a84b5b14c 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -1355,12 +1355,24 @@ model_deployments: - name: google/llama-4-scout-17b-16e-instruct model_name: meta/llama-4-scout-17b-16e-instruct tokenizer_name: meta/llama-4-scout-17b-16e-instruct - max_sequence_length: 128000 + max_sequence_length: 1000000 client_spec: - class_name: "helm.clients.vertexai_client.VertexAIChatClient" + class_name: "helm.clients.google_genai_client.GoogleGenAIClient" args: - vertexai_model: publishers/meta/models/llama-4-scout-17b-16e-instruct-maas - genai_use_vertexai: False + genai_model: meta/llama-4-scout-17b-16e-instruct-maas + genai_use_vertexai: True + location: us-east5 + + - name: google/llama-4-maverick-17b-128e-instruct + model_name: meta/llama-4-maverick-17b-128e-instruct + tokenizer_name: meta/llama-4-scout-17b-16e-instruct + max_sequence_length: 10000000 + client_spec: + class_name: "helm.clients.google_genai_client.GoogleGenAIClient" + args: + genai_model: meta/llama-4-maverick-17b-128e-instruct-maas + genai_use_vertexai: True + location: us-east5 ## Gemma - name: together/gemma-2b diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index d78ad2c2483..3025564b959 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -2259,6 +2259,15 @@ models: release_date: 2025-04-05 tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: meta/llama-4-maverick-17b-128e-instruct + display_name: Llama 4 Maverick (17Bx128E) Instruct + description: Llama 4 Maverick (17Bx128E) Instruct is part of the Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences using a mixture-of-experts architecture. ([blog](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)) + creator_organization_name: Meta + access: open + num_parameters: 402000000000 + release_date: 2025-04-05 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: meta/llama-4-maverick-17b-128e-instruct-fp8 display_name: Llama 4 Maverick (17Bx128E) Instruct FP8 description: Llama 4 Maverick (17Bx128E) Instruct FP8 is part of the Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences using a mixture-of-experts architecture. ([blog](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)) From bddb97ed6d2af0664b3bf20dbf9865e4e00fc6f6 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 1 Apr 2026 14:45:49 -0700 Subject: [PATCH 53/89] Add Qwen3.5 models on Together (#4152) --- src/helm/config/model_deployments.yaml | 14 ++++++++++++++ src/helm/config/model_metadata.yaml | 18 ++++++++++++++++++ src/helm/config/tokenizer_configs.yaml | 12 ++++++++++++ 3 files changed, 44 insertions(+) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index d1a84b5b14c..e50125f1b98 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -4238,6 +4238,20 @@ model_deployments: # The model name is case sensitive on Together AI for this model together_model: Qwen/Qwen3-235B-A22B-Thinking-2507 + - name: together/qwen3.5-397b-a17b + model_name: qwen/qwen3.5-397b-a17b + tokenizer_name: qwen/qwen3.5-397b-a17b + max_sequence_length: 262144 + client_spec: + class_name: "helm.clients.together_client.TogetherChatClient" + + - name: together/qwen3.5-9b + model_name: qwen/qwen3.5-9b + tokenizer_name: qwen/qwen3.5-9b + max_sequence_length: 262144 + client_spec: + class_name: "helm.clients.together_client.TogetherChatClient" + - name: huggingface/qwen2.5-7b-instruct-4bit model_name: qwen/qwen2.5-7b-instruct tokenizer_name: qwen/qwen2.5-7b-instruct diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index 3025564b959..d8b4470a232 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -3934,6 +3934,24 @@ models: release_date: 2025-07-21 # https://x.com/Alibaba_Qwen/status/1947344511988076547 tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: qwen/qwen3.5-397b-a17b + display_name: Qwen3.5 397B A17B + description: Qwen3.5 397B A17B uses a hybrid architecture that fuses linear attention (via Gated Delta Networks) with a sparse mixture-of-experts. ([blog](https://qwen.ai/blog?id=qwen3.5)) + creator_organization_name: Qwen + access: open + num_parameters: 403000000000 + release_date: 2026-02-15 + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: qwen/qwen3.5-9b + display_name: Qwen3.5 9B + description: Qwen3.5 9B A17B uses a hybrid architecture that fuses linear attention (via Gated Delta Networks) with a sparse mixture-of-experts. ([blog](https://qwen.ai/blog?id=qwen3.5)) + creator_organization_name: Qwen + access: open + num_parameters: 10000000000 + release_date: 2026-03-02 # Source: https://github.com/QwenLM/Qwen3.5/blob/7e103aebdbff11c98cc1cd6a2a568988cfd38168/README.md + tags: [TEXT_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: qwen/qwq-32b-preview display_name: QwQ (32B Preview) description: QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. ([blog post](https://qwenlm.github.io/blog/qwq-32b-preview/)). diff --git a/src/helm/config/tokenizer_configs.yaml b/src/helm/config/tokenizer_configs.yaml index 876c2a4039e..11da37bda0a 100644 --- a/src/helm/config/tokenizer_configs.yaml +++ b/src/helm/config/tokenizer_configs.yaml @@ -798,6 +798,18 @@ tokenizer_configs: end_of_text_token: "<|im_end|>" prefix_token: "" + - name: qwen/qwen3.5-397b-a17b + tokenizer_spec: + class_name: "helm.tokenizers.huggingface_tokenizer.HuggingFaceTokenizer" + end_of_text_token: "<|im_end|>" + prefix_token: "" + + - name: qwen/qwen3.5-9b + tokenizer_spec: + class_name: "helm.tokenizers.huggingface_tokenizer.HuggingFaceTokenizer" + end_of_text_token: "<|im_end|>" + prefix_token: "" + - name: qwen/qwq-32b-preview tokenizer_spec: class_name: "helm.tokenizers.huggingface_tokenizer.HuggingFaceTokenizer" From 91ca75ad028b6c6b3bf51ed00f8c065888363224 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 1 Apr 2026 16:18:40 -0700 Subject: [PATCH 54/89] Remove requirements.txt (#4154) --- requirements.txt | 190 ----------------------------------------------- 1 file changed, 190 deletions(-) delete mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 82862e4c0ee..00000000000 --- a/requirements.txt +++ /dev/null @@ -1,190 +0,0 @@ -absl-py==2.3.1 -aiohappyeyeballs==2.6.1 -aiohttp==3.13.3 -aiosignal==1.4.0 -annotated-types==0.7.0 -anyio==4.12.1 -async-timeout==5.0.1 -attrs==25.4.0 -black==24.3.0 -blis==1.3.3 -bottle==0.12.25 -catalogue==2.0.10 -cattrs==22.2.0 -certifi==2026.1.4 -cffi==2.0.0 -cfgv==3.5.0 -charset-normalizer==3.4.4 -click==8.3.1 -cloudpathlib==0.23.0 -cohere==5.20.1 -colorama==0.4.6 -colorcet==3.1.0 -colorlog==6.10.1 -confection==0.1.5 -contourpy==1.3.2 -cryptography==46.0.6 -cycler==0.12.1 -cymem==2.0.13 -dacite==1.9.2 -datasets==3.6.0 -dill==0.3.8 -distlib==0.4.0 -distro==1.9.0 -en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl -eval-type-backport==0.2.2 -exceptiongroup==1.3.1 -fastavro==1.12.1 -filelock==3.20.3 -flake8==7.3.0 -fonttools==4.61.1 -frozenlist==1.8.0 -fsspec==2025.3.0 -google-api-core==2.29.0 -google-api-python-client==2.188.0 -google-auth==2.47.0 -google-auth-httplib2==0.3.0 -googleapis-common-protos==1.72.0 -gradio-client==1.14.0 -gunicorn==23.0.0 -h11==0.16.0 -hf-xet==1.2.0 -httpcore==1.0.9 -httplib2==0.31.1 -httpx==0.28.1 -huggingface-hub==0.36.0 -identify==2.6.16 -idna==3.11 -immutabledict==4.2.2 -iniconfig==2.3.0 -jinja2==3.1.6 -jiter==0.12.0 -joblib==1.5.3 -kiwisolver==1.4.9 -langdetect==1.0.9 -llvmlite==0.46.0 -lxml==5.4.0 -mako==1.3.10 -markdown-it-py==4.0.0 -markupsafe==3.0.3 -matplotlib==3.10.8 -mccabe==0.7.0 -mdurl==0.1.2 -mpmath==1.3.0 -multidict==6.7.0 -multiprocess==0.70.16 -murmurhash==1.0.15 -mypy==1.16.0 -mypy-extensions==1.1.0 -networkx==3.4.2 -nltk==3.9.2 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"dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 3 Apr 2026 13:16:50 -0700 Subject: [PATCH 56/89] Bump lodash from 4.17.23 to 4.18.1 in /helm-frontend (#4158) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- helm-frontend/yarn.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index 51380104e65..be82e4ee4d8 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -4080,9 +4080,9 @@ lodash.merge@^4.6.2: integrity sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ== lodash@^4.17.21: - version "4.17.23" - resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.23.tgz#f113b0378386103be4f6893388c73d0bde7f2c5a" - integrity sha512-LgVTMpQtIopCi79SJeDiP0TfWi5CNEc/L/aRdTh3yIvmZXTnheWpKjSZhnvMl8iXbC1tFg9gdHHDMLoV7CnG+w== + version "4.18.1" + resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.18.1.tgz#ff2b66c1f6326d59513de2407bf881439812771c" + integrity sha512-dMInicTPVE8d1e5otfwmmjlxkZoUpiVLwyeTdUsi/Caj/gfzzblBcCE5sRHV/AsjuCmxWrte2TNGSYuCeCq+0Q== loose-envify@^1.1.0, loose-envify@^1.4.0: version "1.4.0" From 0b6950068054059bb9f1a4508b9fd7fe4509fc25 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 3 Apr 2026 13:25:50 -0700 Subject: [PATCH 57/89] Set extra-build-dependencies for openai-whisper (#4159) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 7bf3a25e701..1505c70c2f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -462,7 +462,7 @@ override-dependencies = [ ] [tool.uv.extra-build-dependencies] -openai-whisper = ["pkg_resources"] +openai-whisper = ["setuptools==80.10.2"] [tool.setuptools] package-dir = { "" = "src" } From 9a9eaec108030c184511edc5120385770a8e7210 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 3 Apr 2026 13:33:35 -0700 Subject: [PATCH 58/89] Default suite to "default" in helm-run and helm-summarize (#4155) --- src/helm/benchmark/presentation/summarize.py | 2 +- src/helm/benchmark/run.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/helm/benchmark/presentation/summarize.py b/src/helm/benchmark/presentation/summarize.py index cbddd26ea94..586bf48fcd2 100644 --- a/src/helm/benchmark/presentation/summarize.py +++ b/src/helm/benchmark/presentation/summarize.py @@ -1430,7 +1430,7 @@ def summarize(args): release = args.release suites = args.suites else: - raise ValueError("Exactly one of --release or --suite must be specified.") + suite = "default" schema_path: Optional[str] if args.auto_generate_schema: diff --git a/src/helm/benchmark/run.py b/src/helm/benchmark/run.py index 406130346d8..6c5218a7cc4 100644 --- a/src/helm/benchmark/run.py +++ b/src/helm/benchmark/run.py @@ -251,7 +251,7 @@ def add_run_args(parser: argparse.ArgumentParser): "--suite", type=str, help="Name of the suite this run belongs to (default is today's date).", - required=True, + default="default", ) parser.add_argument( "--local-path", From c3d6c1813224c481ee6e5f2a37927636b2c5be96 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 3 Apr 2026 13:39:21 -0700 Subject: [PATCH 59/89] Ignore type errors caused by upgrading transformers (#4160) --- pyproject.toml | 2 +- src/helm/clients/huggingface_client.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1505c70c2f6..409aa656ab8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,7 @@ dependencies = [ "uncertainty-calibration~=0.1.4", "scikit-learn>=1.1", # Models and Metrics Extras - "transformers~=4.53", # For anthropic_client, vision_language.huggingface_vlm_client, huggingface_client, huggingface_tokenizer, test_openai_token_cost_estimator, model_summac (via summarization_metrics) + "transformers>=4.53.0,<6.0.0", # For anthropic_client, vision_language.huggingface_vlm_client, huggingface_client, huggingface_tokenizer, test_openai_token_cost_estimator, model_summac (via summarization_metrics) "torch>=1.13.1,<3.0.0", # For huggingface_client, yalm_tokenizer, model_summac (via summarization_metrics) "torchvision>=0.14.1,<3.0.0", # For huggingface_client, yalm_tokenizer, model_summac (via summarization_metrics) ] diff --git a/src/helm/clients/huggingface_client.py b/src/helm/clients/huggingface_client.py index cfc3b862ce4..4347e8b6f2c 100644 --- a/src/helm/clients/huggingface_client.py +++ b/src/helm/clients/huggingface_client.py @@ -92,7 +92,7 @@ def __init__( self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **kwargs) else: self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **kwargs).to( - self.device + self.device # type: ignore ) self.wrapped_tokenizer = wrapped_tokenizer @@ -143,8 +143,8 @@ def serve_request(self, raw_request: HuggingFaceRequest) -> Dict: stopping_criteria=stopping_criteria, pad_token_id=pad_token_id, ) - sequences = output.sequences - scores = output.scores + sequences = output.sequences # type: ignore + scores = output.scores # type: ignore prompt_tokens_logprobs = [] if compute_logprobs_only: From 561be340d3d14681f6cbb86e5745dda1c466fe7e Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 3 Apr 2026 13:45:37 -0700 Subject: [PATCH 60/89] Upgrade dependencies (#4162) Co-authored-by: yifanmai <185227+yifanmai@users.noreply.github.com> --- uv.lock | 5936 +++++++++++++++++++++++++++---------------------------- 1 file changed, 2930 insertions(+), 3006 deletions(-) diff --git a/uv.lock b/uv.lock index e372dab32a0..f08735908d3 100644 --- a/uv.lock +++ b/uv.lock @@ -2,16 +2,10 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'linux'", - "python_full_version >= '3.14' and 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b/src/helm/benchmark/metrics/summac/model_summac.py index 74c83668614..7b94e7fcc51 100644 --- a/src/helm/benchmark/metrics/summac/model_summac.py +++ b/src/helm/benchmark/metrics/summac/model_summac.py @@ -1,3 +1,5 @@ +# mypy: ignore-errors + ############################################### # Source: https://github.com/tingofurro/summac ############################################### From f189d513a7b264ec9d7a4385762d80bdbfb58ac8 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 6 Apr 2026 16:21:41 -0700 Subject: [PATCH 62/89] Lazily load model in HuggingFaceClient (#4172) --- src/helm/clients/huggingface_client.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/helm/clients/huggingface_client.py b/src/helm/clients/huggingface_client.py index 4347e8b6f2c..9ac68ec63e9 100644 --- a/src/helm/clients/huggingface_client.py +++ b/src/helm/clients/huggingface_client.py @@ -325,16 +325,16 @@ def make_request(self, request: Request) -> RequestResult: pretrained_model_name_or_path = ( self._pretrained_model_name_or_path if self._pretrained_model_name_or_path else request.model ) - huggingface_model: HuggingFaceServer = HuggingFaceServerFactory.get_server( - helm_model_name=request.model, - pretrained_model_name_or_path=pretrained_model_name_or_path, - wrapped_tokenizer=self._wrapped_tokenizer, - **self._kwargs, - ) try: def do_it() -> Dict[str, Any]: + huggingface_model: HuggingFaceServer = HuggingFaceServerFactory.get_server( + helm_model_name=request.model, + pretrained_model_name_or_path=pretrained_model_name_or_path, + wrapped_tokenizer=self._wrapped_tokenizer, + **self._kwargs, + ) return huggingface_model.serve_request(raw_request) cache_key = CachingClient.make_cache_key(raw_request, request) From d76090e1741cc31f23ac17bb76de260aa5a04aa8 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 6 Apr 2026 17:50:14 -0700 Subject: [PATCH 63/89] Parse Arabic content generation annotator using a more permissive regex (#4173) --- .../arabic_content_generation_annotator.py | 15 ++++++++------- ...rabic_content_generation_relative_annotator.py | 15 ++++++++------- ...bic_content_generation_similarity_annotator.py | 15 ++++++++------- 3 files changed, 24 insertions(+), 21 deletions(-) diff --git a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py index 84f3ac7c214..e58a3804803 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py @@ -1,3 +1,4 @@ +import re from typing import Any, Dict, Union from helm.benchmark.adaptation.request_state import RequestState @@ -91,10 +92,10 @@ def annotate(self, request_state: RequestState) -> Any: return annotations def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, Union[int, str]]: - result: Dict[str, Union[int, str]] = {} - for line in annotator_response_text.split("\n"): - if line.startswith(_REASONING_TAG): - result["reasoning"] = line.removeprefix(_REASONING_TAG).strip() - elif line.startswith(_SCORE_TAG): - result["score"] = int(line.removeprefix(_SCORE_TAG).strip()) - return result + match = re.search(r"REASONING:\s*(.*)\s*SCORE:\s*(\d+)", annotator_response_text) + if not match: + raise ValueError(f"Could not parse annotator response: '{annotator_response_text}'") + return { + "reasoning": match.group(1), + "score": match.group(2), + } diff --git a/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py index 46a51dc7811..f26c5f6fa60 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py @@ -1,3 +1,4 @@ +import re from typing import Any, Dict, Union from helm.benchmark.adaptation.request_state import RequestState @@ -72,10 +73,10 @@ def annotate(self, request_state: RequestState) -> Any: return {"prompt": annotator_prompt, **self._parse_annotator_response(annotator_response_text)} def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, Union[int, str]]: - result: Dict[str, Union[int, str]] = {} - for line in annotator_response_text.split("\n"): - if line.startswith(_REASONING_TAG): - result["reasoning"] = line.removeprefix(_REASONING_TAG).strip() - elif line.startswith(_SCORE_TAG): - result["score"] = int(line.removeprefix(_SCORE_TAG).strip()) - return result + match = re.search(r"REASONING:\s*(.*)\s*SCORE:\s*(\d+)", annotator_response_text) + if not match: + raise ValueError(f"Could not parse annotator response: '{annotator_response_text}'") + return { + "reasoning": match.group(1), + "score": match.group(2), + } diff --git a/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py index efb21b9d7a4..de5e91afd28 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py @@ -1,3 +1,4 @@ +import re from typing import Any, Dict, Union from helm.benchmark.adaptation.request_state import RequestState @@ -81,10 +82,10 @@ def annotate(self, request_state: RequestState) -> Any: return annotations def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, Union[int, str]]: - result: Dict[str, Union[int, str]] = {} - for line in annotator_response_text.split("\n"): - if line.startswith(_REASONING_TAG): - result["reasoning"] = line.removeprefix(_REASONING_TAG).strip() - elif line.startswith(_SCORE_TAG): - result["score"] = int(line.removeprefix(_SCORE_TAG).strip()) - return result + match = re.search(r"REASONING:\s*(.*)\s*SCORE:\s*(\d+)", annotator_response_text) + if not match: + raise ValueError(f"Could not parse annotator response: '{annotator_response_text}'") + return { + "reasoning": match.group(1), + "score": match.group(2), + } From 987e96c4627c1ee2303c41145ee754249e034662 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 6 Apr 2026 18:09:08 -0700 Subject: [PATCH 64/89] Parse int in Arabic conten generation annotator (#4174) --- .../benchmark/annotation/arabic_content_generation_annotator.py | 2 +- .../annotation/arabic_content_generation_relative_annotator.py | 2 +- .../arabic_content_generation_similarity_annotator.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py index e58a3804803..3585fce4fe2 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_annotator.py @@ -97,5 +97,5 @@ def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, U raise ValueError(f"Could not parse annotator response: '{annotator_response_text}'") return { "reasoning": match.group(1), - "score": match.group(2), + "score": int(match.group(2)), } diff --git a/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py index f26c5f6fa60..5e460f7d2c5 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_relative_annotator.py @@ -78,5 +78,5 @@ def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, U raise ValueError(f"Could not parse annotator response: '{annotator_response_text}'") return { "reasoning": match.group(1), - "score": match.group(2), + "score": int(match.group(2)), } diff --git a/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py b/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py index de5e91afd28..289ee568277 100644 --- a/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py +++ b/src/helm/benchmark/annotation/arabic_content_generation_similarity_annotator.py @@ -87,5 +87,5 @@ def _parse_annotator_response(self, annotator_response_text: str) -> Dict[str, U raise ValueError(f"Could not parse annotator response: '{annotator_response_text}'") return { "reasoning": match.group(1), - "score": match.group(2), + "score": int(match.group(2)), } From 133edde18e2bda092e43522ee95d18572df13d77 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 7 Apr 2026 16:32:36 -0700 Subject: [PATCH 65/89] Add metadata for HEIM scenarios (#4169) --- .../common_syntactic_processes_scenario.py | 14 +++++- .../image_generation/cub200_scenario.py | 25 +++++++++- .../image_generation/daily_dalle_scenario.py | 13 ++++- .../demographic_stereotypes_scenario.py | 14 +++++- .../image_generation/detection_scenario.py | 23 ++++++++- .../image_generation/draw_bench_scenario.py | 45 ++++++++++++++++- .../image_generation/i2p_scenario.py | 15 +++++- .../image_generation/landing_page_scenario.py | 13 ++++- .../image_generation/logos_scenario.py | 13 ++++- .../magazine_cover_scenario.py | 13 ++++- .../mental_disorders_scenario.py | 13 ++++- .../image_generation/mscoco_scenario.py | 12 +++++ .../parti_prompts_scenario.py | 49 ++++++++++++++++++- .../relational_understanding_scenario.py | 15 +++++- ...significant_historical_figures_scenario.py | 13 ++++- .../image_generation/winoground_scenario.py | 24 ++++++++- 16 files changed, 299 insertions(+), 15 deletions(-) diff --git a/src/helm/benchmark/scenarios/image_generation/common_syntactic_processes_scenario.py b/src/helm/benchmark/scenarios/image_generation/common_syntactic_processes_scenario.py index 2db16758407..a8159455587 100644 --- a/src/helm/benchmark/scenarios/image_generation/common_syntactic_processes_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/common_syntactic_processes_scenario.py @@ -1,6 +1,7 @@ from typing import List, Dict -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class CommonSyntacticProcessesScenario(Scenario): @@ -103,3 +104,14 @@ def get_instances(self, _) -> List[Instance]: for prompt, phenomenon in self.PROMPT_TO_PHENOMENON.items() if phenomenon == self.phenomenon ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="common_syntactic_processes", + display_name="Common Syntactic Processes", + description="Prompts from 8 different grammatical phenomena " + "([paper](https://arxiv.org/abs/2210.12889)).", + taxonomy=TaxonomyInfo(task="Reasoning", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/cub200_scenario.py b/src/helm/benchmark/scenarios/image_generation/cub200_scenario.py index 9819d02404f..d4f00c7379a 100644 --- a/src/helm/benchmark/scenarios/image_generation/cub200_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/cub200_scenario.py @@ -3,9 +3,19 @@ import pandas as pd +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_file_downloaded, shell -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, Output, Reference, CORRECT_TAG, TEST_SPLIT +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Input, + Output, + Reference, + CORRECT_TAG, + TEST_SPLIT, + ScenarioMetadata, +) class CUB200Scenario(Scenario): @@ -93,3 +103,16 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="cub200", + display_name="Caltech-UCSD Birds-200-2011", + description="Caltech-UCSD Birds-200-2011 is a challenging dataset of 200 bird species with " + "10 captions for each bird " + "([paper](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf), " + "[paper](https://arxiv.org/abs/1711.10485)).", + taxonomy=TaxonomyInfo(task="Image quality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/daily_dalle_scenario.py b/src/helm/benchmark/scenarios/image_generation/daily_dalle_scenario.py index 5e5c95f3846..861dc53fc02 100644 --- a/src/helm/benchmark/scenarios/image_generation/daily_dalle_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/daily_dalle_scenario.py @@ -1,6 +1,7 @@ from typing import List -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class DailyDallEScenario(Scenario): @@ -122,3 +123,13 @@ class DailyDallEScenario(Scenario): def get_instances(self, _) -> List[Instance]: return [Instance(Input(text=prompt), references=[], split=TEST_SPLIT) for prompt in self.PROMPTS] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="daily_dalle", + display_name="dailydall.e", + description="DALL-E 2 prompts from [Chad Nelson's " "Instagram](https://www.instagram.com/dailydall.e/)", + taxonomy=TaxonomyInfo(task="Originality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/demographic_stereotypes_scenario.py b/src/helm/benchmark/scenarios/image_generation/demographic_stereotypes_scenario.py index b023bcf8ba1..dbfe54674b6 100644 --- a/src/helm/benchmark/scenarios/image_generation/demographic_stereotypes_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/demographic_stereotypes_scenario.py @@ -1,6 +1,7 @@ from typing import List, Dict -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class DemographicStereotypesScenario(Scenario): @@ -80,3 +81,14 @@ def get_instances(self, _) -> List[Instance]: Instance(Input(text=f"a photo of the face of {person}"), references=[], split=TEST_SPLIT) for person in self.persons ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="demographic_stereotypes", + display_name="Demographic Stereotypes", + description='Simple user prompts ("A photo of the face of...") generate images perpetuating ' + "dangerous racial, ethnic, gendered, class, and intersectional stereotypes.", + taxonomy=TaxonomyInfo(task="Unbiasedness", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/detection_scenario.py b/src/helm/benchmark/scenarios/image_generation/detection_scenario.py index e8f219f394b..03e50f06e33 100644 --- a/src/helm/benchmark/scenarios/image_generation/detection_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/detection_scenario.py @@ -3,8 +3,18 @@ import os import csv +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, Reference, Output, CORRECT_TAG +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Input, + TEST_SPLIT, + Reference, + Output, + CORRECT_TAG, + ScenarioMetadata, +) class DetectionScenario(Scenario): @@ -81,3 +91,14 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="detection", + display_name="Detection (PaintSkills)", + description="A compositional diagnostic dataset an evaluation toolkit that measures visual " + "reasoning skills ([paper](https://arxiv.org/abs/2202.04053)).", + taxonomy=TaxonomyInfo(task="Reasoning", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/draw_bench_scenario.py b/src/helm/benchmark/scenarios/image_generation/draw_bench_scenario.py index 3890b2f7699..0a710bd4871 100644 --- a/src/helm/benchmark/scenarios/image_generation/draw_bench_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/draw_bench_scenario.py @@ -2,8 +2,9 @@ import os from typing import List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class DrawBenchScenario(Scenario): @@ -72,3 +73,45 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + if self.category in ["Colors", "Text", "Rare"]: + return ScenarioMetadata( + name="draw_bench_image_quality", + display_name="DrawBench (image quality categories)", + description="A comprehensive and challenging set of prompts that support the evaluation and " + "comparison of text-to-image models.", + taxonomy=TaxonomyInfo(task="Image quality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) + elif self.category == "Reddit": + return ScenarioMetadata( + name="draw_bench_knowledge", + display_name="DrawBench (knowledge categories)", + description="A comprehensive and challenging set of prompts that support the evaluation and " + "comparison of text-to-image models.", + taxonomy=TaxonomyInfo(task="Knowledge", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) + elif self.category == "Misspellings": + return ScenarioMetadata( + name="draw_bench_reasoning", + display_name="DrawBench (robustness categories)", + description="A comprehensive and challenging set of prompts that support the evaluation and " + "comparison of text-to-image models.", + taxonomy=TaxonomyInfo(task="Robustness", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) + else: + return ScenarioMetadata( + name="draw_bench_reasoning", + display_name="DrawBench (reasoning categories)", + description="A comprehensive and challenging set of prompts that support the evaluation and " + "comparison of text-to-image models.", + taxonomy=TaxonomyInfo(task="Reasoning", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/i2p_scenario.py b/src/helm/benchmark/scenarios/image_generation/i2p_scenario.py index 3aefb67965c..8596924a656 100644 --- a/src/helm/benchmark/scenarios/image_generation/i2p_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/i2p_scenario.py @@ -2,7 +2,8 @@ from datasets import load_dataset -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class I2PScenario(Scenario): @@ -55,3 +56,15 @@ def get_instances(self, _) -> List[Instance]: and row["prompt_toxicity"] < 0.5 and (self.category in row["categories"] or self.category == self.ALL_CATEGORY) ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="i2p", + display_name="Inappropriate Image Prompts (I2P)", + description="Contains real user prompts for generative text-to-image prompts that are " + "likely to produce inappropriate images " + "([paper](https://arxiv.org/abs/2211.05105)).", + taxonomy=TaxonomyInfo(task="Toxicity mitigation", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/landing_page_scenario.py b/src/helm/benchmark/scenarios/image_generation/landing_page_scenario.py index eecc9b39f16..9922cb02a07 100644 --- a/src/helm/benchmark/scenarios/image_generation/landing_page_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/landing_page_scenario.py @@ -1,6 +1,7 @@ from typing import List -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class LandingPageScenario(Scenario): @@ -44,3 +45,13 @@ def get_instances(self, _) -> List[Instance]: for app_type in self.APPLICATION_TYPES for platform in self.PLATFORMS ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="landing_page", + display_name="Landing Page", + description="Prompts to generate landing pages for mobile or web applications.", + taxonomy=TaxonomyInfo(task="Originality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/logos_scenario.py b/src/helm/benchmark/scenarios/image_generation/logos_scenario.py index 863928f7daa..085d3186ff0 100644 --- a/src/helm/benchmark/scenarios/image_generation/logos_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/logos_scenario.py @@ -1,6 +1,7 @@ from typing import List -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class LogosScenario(Scenario): @@ -221,3 +222,13 @@ def get_instances(self, _) -> List[Instance]: Instance(Input(text=f"a logo of {description}"), references=[], split=TEST_SPLIT) for description in self.COMPANY_DESCRIPTIONS ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="logos", + display_name="Logos", + description="Prompts to generate logos for brands and companies", + taxonomy=TaxonomyInfo(task="Originality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/magazine_cover_scenario.py b/src/helm/benchmark/scenarios/image_generation/magazine_cover_scenario.py index 0d33d9bdaee..30b04042e50 100644 --- a/src/helm/benchmark/scenarios/image_generation/magazine_cover_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/magazine_cover_scenario.py @@ -1,6 +1,7 @@ from typing import List -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class MagazineCoverScenario(Scenario): @@ -89,3 +90,13 @@ def construct_prompt(headline: str) -> str: Instance(Input(text=construct_prompt(headline)), references=[], split=TEST_SPLIT) for headline in self.HEADLINES ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="magazine_cover", + display_name="Magazine Cover Photos", + description="Prompts to generate magazine cover photos", + taxonomy=TaxonomyInfo(task="Originality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/mental_disorders_scenario.py b/src/helm/benchmark/scenarios/image_generation/mental_disorders_scenario.py index b03d9bd8fd9..a971b61fb3f 100644 --- a/src/helm/benchmark/scenarios/image_generation/mental_disorders_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/mental_disorders_scenario.py @@ -1,6 +1,7 @@ from typing import List -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class MentalDisordersScenario(Scenario): @@ -44,3 +45,13 @@ def get_instances(self, _) -> List[Instance]: ) for mental_disorder in self.MENTAL_DISORDERS ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mental_disorders", + display_name="Mental Disorders", + description="Prompt text-to-image models with mental disorders listed by the World Health " "Organization", + taxonomy=TaxonomyInfo(task="Unbiasedness", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/mscoco_scenario.py b/src/helm/benchmark/scenarios/image_generation/mscoco_scenario.py index a3e975c683f..867d08350a3 100644 --- a/src/helm/benchmark/scenarios/image_generation/mscoco_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/mscoco_scenario.py @@ -3,6 +3,7 @@ from collections import defaultdict from typing import Any, Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded from helm.common.media_object import MediaObject, MultimediaObject from helm.benchmark.scenarios.scenario import ( @@ -14,6 +15,7 @@ CORRECT_TAG, TRAIN_SPLIT, VALID_SPLIT, + ScenarioMetadata, ) @@ -89,3 +91,13 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mscoco", + display_name="MS-COCO (all)", + description="Common Objects in Context ([paper](https://arxiv.org/abs/1405.0312)).", + taxonomy=TaxonomyInfo(task="Image quality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/parti_prompts_scenario.py b/src/helm/benchmark/scenarios/image_generation/parti_prompts_scenario.py index 4300c6786a0..20a4ea0b761 100644 --- a/src/helm/benchmark/scenarios/image_generation/parti_prompts_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/parti_prompts_scenario.py @@ -2,8 +2,9 @@ import os from typing import List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class PartiPromptsScenario(Scenario): @@ -92,3 +93,49 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + if self.category == "Illustrations": + return ScenarioMetadata( + name="parti_prompts_reasoning", + display_name="PartiPrompts (reasoning categories)", + description="PartiPrompts (P2) is a set of 1600 diverse English prompts that allow us to " + "more comprehensively evaluate and test the limits of text-to-image synthesis " + "models.", + taxonomy=TaxonomyInfo(task="Reasoning", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) + elif self.category == "World": + return ScenarioMetadata( + name="parti_prompts_knowledge", + display_name="PartiPrompts (knowledge categories)", + description="PartiPrompts (P2) is a set of 1600 diverse English prompts that allow us to " + "more comprehensively evaluate and test the limits of text-to-image synthesis " + "models.", + taxonomy=TaxonomyInfo(task="Knowledge", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) + elif self.category == "Abstract": + return ScenarioMetadata( + name="parti_prompts_knowledge", + display_name="PartiPrompts (extra categories)", + description="PartiPrompts (P2) is a set of 1600 diverse English prompts that allow us to " + "more comprehensively evaluate and test the limits of text-to-image synthesis " + "models.", + taxonomy=TaxonomyInfo(task="Extra", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) + else: + return ScenarioMetadata( + name="parti_prompts_image_quality", + display_name="PartiPrompts (image quality categories)", + description="PartiPrompts (P2) is a set of 1600 diverse English prompts that allow us to " + "more comprehensively evaluate and test the limits of text-to-image synthesis " + "models.", + taxonomy=TaxonomyInfo(task="Image quality", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/relational_understanding_scenario.py b/src/helm/benchmark/scenarios/image_generation/relational_understanding_scenario.py index 8e26968d20f..3738a94705a 100644 --- a/src/helm/benchmark/scenarios/image_generation/relational_understanding_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/relational_understanding_scenario.py @@ -2,8 +2,9 @@ import csv import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class RelationalUnderstandingScenario(Scenario): @@ -50,3 +51,15 @@ def get_instances(self, output_path: str) -> List[Instance]: seen_prompts.add(prompt) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="relational_understanding", + display_name="Relational Understanding", + description="Based on existing cognitive, linguistic, and developmental literature, the " + "authors created a set of 15 relations (8 physical, 7 agentic) and a set of 12 " + "entities (6 objects, 6 agents)", + taxonomy=TaxonomyInfo(task="Reasoning", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/time_most_significant_historical_figures_scenario.py b/src/helm/benchmark/scenarios/image_generation/time_most_significant_historical_figures_scenario.py index 7c82f414e21..7ed36bcdc02 100644 --- a/src/helm/benchmark/scenarios/image_generation/time_most_significant_historical_figures_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/time_most_significant_historical_figures_scenario.py @@ -1,6 +1,7 @@ from typing import List -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo +from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, TEST_SPLIT, ScenarioMetadata class TIMEMostSignificantHistoricalFigures(Scenario): @@ -122,3 +123,13 @@ def get_instances(self, _) -> List[Instance]: Instance(Input(text=historical_figure), references=[], split=TEST_SPLIT) for historical_figure in self.HISTORICAL_FIGURES ] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="time_most_significant_historical_figures", + display_name="TIME's most significant historical figures", + description='People from TIME\'s "The 100 Most Significant Figures in History" list', + taxonomy=TaxonomyInfo(task="Knowledge", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/image_generation/winoground_scenario.py b/src/helm/benchmark/scenarios/image_generation/winoground_scenario.py index 48622aa3c7b..7c3d1ff91a2 100644 --- a/src/helm/benchmark/scenarios/image_generation/winoground_scenario.py +++ b/src/helm/benchmark/scenarios/image_generation/winoground_scenario.py @@ -3,10 +3,20 @@ from datasets import load_dataset +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import get_file_name from helm.common.images_utils import copy_image from helm.common.media_object import MediaObject, MultimediaObject -from helm.benchmark.scenarios.scenario import Scenario, Instance, Input, Output, Reference, CORRECT_TAG, TEST_SPLIT +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Input, + Output, + Reference, + CORRECT_TAG, + TEST_SPLIT, + ScenarioMetadata, +) class WinogroundScenario(Scenario): @@ -60,3 +70,15 @@ def get_instances(self, output_path: str) -> List[Instance]: ) ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="winoground", + display_name="Winoground", + description="Winoground is a novel task and dataset for evaluating the ability of vision " + "and language models to conduct visio-linguistic compositional reasoning " + "([paper](https://arxiv.org/abs/2204.03162)).", + taxonomy=TaxonomyInfo(task="Reasoning", what=None, when=None, who=None, language=None), + main_metric="unknown", + main_split="test", + ) From 01074880e75914430d48441d840ac9a75917263f Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 7 Apr 2026 16:33:35 -0700 Subject: [PATCH 66/89] Add arabic_legal_rag and arabic_legal_qa to Arabic Enterprise run entries --- .../benchmark/presentation/run_entries_arabic_enterprise.conf | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf b/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf index 18549b4d577..2d0494e9f9f 100644 --- a/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf +++ b/src/helm/benchmark/presentation/run_entries_arabic_enterprise.conf @@ -2,6 +2,8 @@ entries: [ {description: "arabic_content_generation:category=all"}, + {description: "arabic_legal_qa"}, + {description: "arabic_legal_rag"}, {description: "arabic_finance_bool"}, {description: "arabic_finance_calculation"}, {description: "arabic_finance_mcq"}, From 220fd6905de260194219f673d054472f0013cc42 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 7 Apr 2026 16:44:59 -0700 Subject: [PATCH 67/89] Add metadata for Arabic scenarios (#4170) --- .../benchmark/scenarios/alghafa_scenario.py | 18 ++++++++++++++++++ .../benchmark/scenarios/alrage_scenario.py | 18 ++++++++++++++++++ .../scenarios/arabic_exams_scenario.py | 18 ++++++++++++++++++ .../scenarios/arabic_mmlu_scenario.py | 18 ++++++++++++++++++ .../benchmark/scenarios/aratrust_scenario.py | 18 ++++++++++++++++++ .../scenarios/madinah_qa_scenario.py | 18 ++++++++++++++++++ .../mbzuai_human_translated_arabic_mmlu.py | 19 +++++++++++++++++++ 7 files changed, 127 insertions(+) diff --git a/src/helm/benchmark/scenarios/alghafa_scenario.py b/src/helm/benchmark/scenarios/alghafa_scenario.py index 70045e1989e..ad0dedcde6a 100644 --- a/src/helm/benchmark/scenarios/alghafa_scenario.py +++ b/src/helm/benchmark/scenarios/alghafa_scenario.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -13,6 +14,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -124,3 +126,19 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="alghafa", + display_name="AlGhafa", + description="An Arabic language multiple choice evaluation benchmark derived from publicly available NLP datasets ([paper](https://aclanthology.org/2023.arabicnlp-1.21/))", + taxonomy=TaxonomyInfo( + task="multiple choice question answering", + what="Various", + when="before 2023", + who="Various", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/alrage_scenario.py b/src/helm/benchmark/scenarios/alrage_scenario.py index 2db535250a8..62727d9c726 100644 --- a/src/helm/benchmark/scenarios/alrage_scenario.py +++ b/src/helm/benchmark/scenarios/alrage_scenario.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -52,3 +54,19 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="alrage", + display_name="ALRAGE", + description="An Arabic language passage-based open-ended model-graded question answering benchmark that reflects retrieval-augmented generation use cases ([dataset](https://huggingface.co/datasets/OALL/ALRAGE))", + taxonomy=TaxonomyInfo( + task="openbook (RAG) open-ended question answering", + what="passage-based question answering", + when="book authors", + who="before 2025", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/arabic_exams_scenario.py b/src/helm/benchmark/scenarios/arabic_exams_scenario.py index 4fa12423f5d..959baadef01 100644 --- a/src/helm/benchmark/scenarios/arabic_exams_scenario.py +++ b/src/helm/benchmark/scenarios/arabic_exams_scenario.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -13,6 +14,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from helm.common.hierarchical_logger import hwarn @@ -112,3 +114,19 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="arabic_exams", + display_name="EXAMS (Arabic)", + description="The Arabic language subset of the EXAMS multilingual question answering benchmark, which consists of high school exam questions across various school subjects ([paper](https://aclanthology.org/2020.emnlp-main.438/))", + taxonomy=TaxonomyInfo( + task="multiple choice question answering", + what="High school examinations", + when="before 2020", + who="High school examinations writers and test-takers", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/arabic_mmlu_scenario.py b/src/helm/benchmark/scenarios/arabic_mmlu_scenario.py index 8aac4b72420..abcc13da511 100644 --- a/src/helm/benchmark/scenarios/arabic_mmlu_scenario.py +++ b/src/helm/benchmark/scenarios/arabic_mmlu_scenario.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -13,6 +14,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -80,3 +82,19 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="arabic_mmlu", + display_name="Arabic MMLU", + description="A native Arabic language question answering benchmark using questions sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions ([dataset](https://huggingface.co/datasets/MBZUAI/ArabicMMLU))", + taxonomy=TaxonomyInfo( + task="question answering", + what="academic questions across various disciplines", + when="before 2024", + who="academic exams writers and takers", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/aratrust_scenario.py b/src/helm/benchmark/scenarios/aratrust_scenario.py index e314386bce7..a9b691a409e 100644 --- a/src/helm/benchmark/scenarios/aratrust_scenario.py +++ b/src/helm/benchmark/scenarios/aratrust_scenario.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -117,3 +119,19 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="aratrust", + display_name="AraTrust", + description="An Arab-region-specific safety evaluation dataset consisting of human-written questions including direct attacks, indirect attacks, and harmless requests with sensitive words ([paper](https://arxiv.org/abs/2410.17040))", + taxonomy=TaxonomyInfo( + task="question answering", + what="academic questions across various disciplines", + when="before 2024", + who="academic exams writers and takers", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/madinah_qa_scenario.py b/src/helm/benchmark/scenarios/madinah_qa_scenario.py index d0b3d179374..264476d2e75 100644 --- a/src/helm/benchmark/scenarios/madinah_qa_scenario.py +++ b/src/helm/benchmark/scenarios/madinah_qa_scenario.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -13,6 +14,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -80,3 +82,19 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="madinah_qa", + display_name="MadinahQA", + description="A question answering benchmark published by MBZUAI that tests knowledge of Arabic language and grammar ([dataset](https://huggingface.co/datasets/MBZUAI/MadinahQA))", + taxonomy=TaxonomyInfo( + task="question answering", + what="academic questions about Arabic language", + when="before 2024", + who="academic exams writers and takers", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/mbzuai_human_translated_arabic_mmlu.py b/src/helm/benchmark/scenarios/mbzuai_human_translated_arabic_mmlu.py index 03134b0ac89..45b4dd4eb6d 100644 --- a/src/helm/benchmark/scenarios/mbzuai_human_translated_arabic_mmlu.py +++ b/src/helm/benchmark/scenarios/mbzuai_human_translated_arabic_mmlu.py @@ -3,6 +3,7 @@ import datasets +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -66,3 +68,20 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mbzuai_human_translated_arabic_mmlu", + display_name="MBZUAI Human-Translated Arabic MMLU", + short_display_name="Translated MMLU", + description="A translation of MMLU to Arabic by human translators published by MBZUAI ([dataset](https://huggingface.co/datasets/MBZUAI/human_translated_arabic_mmlu))", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="math, science, history, etc.", + when="various online sources", + who="before 2021", + language="Arabic", + ), + main_metric="exact_match", + main_split="test", + ) From 1c451fbf931abd0295ef34f02a4da26a7208d29b Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 9 Apr 2026 15:37:53 -0700 Subject: [PATCH 68/89] Add metadata to mmlu_clinical_afr_scenario and winogrande_afr_scenario (#4184) --- .../scenarios/mmlu_clinical_afr_scenario.py | 49 ++++++++++++++++++- .../scenarios/winogrande_afr_scenario.py | 48 +++++++++++++++++- 2 files changed, 95 insertions(+), 2 deletions(-) diff --git a/src/helm/benchmark/scenarios/mmlu_clinical_afr_scenario.py b/src/helm/benchmark/scenarios/mmlu_clinical_afr_scenario.py index 82e0ba8e80d..8df5b1e6036 100644 --- a/src/helm/benchmark/scenarios/mmlu_clinical_afr_scenario.py +++ b/src/helm/benchmark/scenarios/mmlu_clinical_afr_scenario.py @@ -2,9 +2,21 @@ import os from typing import Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded from helm.common.hierarchical_logger import hlog -from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, CORRECT_TAG, Input, Output +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Reference, + TRAIN_SPLIT, + VALID_SPLIT, + TEST_SPLIT, + CORRECT_TAG, + Input, + Output, + ScenarioMetadata, +) class MMLU_Clinical_Afr_Scenario(Scenario): @@ -16,6 +28,20 @@ class MMLU_Clinical_Afr_Scenario(Scenario): description = "Massive Multitask Language Understanding (MMLU) translated into 11 African low-resource languages" tags = ["knowledge", "multiple_choice", "low_resource_languages"] + _LANGUAGE_CODE_TO_NAME = { + "af": "Afrikaans", + "am": "Amharic", + "bm": "Bambara", + "ig": "Igbo", + "nso": "Sepedi", + "sn": "Shona", + "st": "Sesotho", + "tn": "Setswana", + "ts": "Tsonga", + "xh": "Xhosa", + "zu": "Zulu", + } + def __init__(self, subject: str = "clinical_knowledge", lang: str = "af"): super().__init__() self.subject: str = subject @@ -72,3 +98,24 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.extend(self.process_csv(csv_path, splits[split])) return instances + + def get_metadata(self) -> ScenarioMetadata: + language = self._LANGUAGE_CODE_TO_NAME[self.lang] + subject = self.subject.replace("_", " ") + return ScenarioMetadata( + name=f"mmlu_clinical_afr_clinical_knowledge_{self.lang}", + display_name=f"MMLU Clinical Knowledge ({language.title()})", + short_display_name=f"MMLU Clinical Knowledge ({language.title()})", + description=f"The {subject} subject in the Massive Multitask Language Understanding " + "(MMLU) ([Hendrycks et al. 2021](https://arxiv.org/abs/2009.03300)) benchmark " + f"translated to {language} by human translators.", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what=self.subject, + when="before 2021", + who="various online sources", + language=language, + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/winogrande_afr_scenario.py b/src/helm/benchmark/scenarios/winogrande_afr_scenario.py index b89bba5dc53..182b9676b64 100644 --- a/src/helm/benchmark/scenarios/winogrande_afr_scenario.py +++ b/src/helm/benchmark/scenarios/winogrande_afr_scenario.py @@ -2,9 +2,21 @@ import os from typing import Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_file_downloaded from helm.common.hierarchical_logger import hlog -from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, CORRECT_TAG, Input, Output +from helm.benchmark.scenarios.scenario import ( + Scenario, + Instance, + Reference, + TRAIN_SPLIT, + VALID_SPLIT, + TEST_SPLIT, + CORRECT_TAG, + Input, + Output, + ScenarioMetadata, +) class Winogrande_Afr_Scenario(Scenario): @@ -16,6 +28,20 @@ class Winogrande_Afr_Scenario(Scenario): description = "Winogrande (S) translated into 11 African low-resource languages" tags = ["knowledge", "multiple_choice", "low_resource_languages"] + _LANGUAGE_CODE_TO_NAME = { + "af": "Afrikaans", + "am": "Amharic", + "bm": "Bambara", + "ig": "Igbo", + "nso": "Sepedi", + "sn": "Shona", + "st": "Sesotho", + "tn": "Setswana", + "ts": "Tsonga", + "xh": "Xhosa", + "zu": "Zulu", + } + def __init__(self, lang: str = "af"): super().__init__() self.lang: str = lang @@ -76,3 +102,23 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.extend(self.process_csv(csv_path, splits[split], split)) return instances + + def get_metadata(self) -> ScenarioMetadata: + language = self._LANGUAGE_CODE_TO_NAME[self.lang] + return ScenarioMetadata( + name=f"winogrande_afr_{self.lang}", + display_name=f"Winogrande ({language})", + short_display_name=f"Winogrande ({language})", + description="The multiple-choice reasoning benchmark Winogrande ([Sakaguchi et al. " + f"2021](https://arxiv.org/abs/1907.10641)) translated to {language} by human " + "translators", + taxonomy=TaxonomyInfo( + task="multiple-choice reasoning", + what="pronoun resolution", + when="before 2019", + who="workers on Amazon Mechanical Turk", + language=language, + ), + main_metric="exact_match", + main_split="test", + ) From f842f1944894ef62dd049c85a55b4576c81cf6f6 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Fri, 10 Apr 2026 12:52:38 -0700 Subject: [PATCH 69/89] Switch documentation to use gcloud cp instead of gcloud rsync (#4188) --- docs/downloading_raw_results.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/downloading_raw_results.md b/docs/downloading_raw_results.md index 6ed21a2457a..c85391de6ba 100644 --- a/docs/downloading_raw_results.md +++ b/docs/downloading_raw_results.md @@ -24,7 +24,7 @@ Locations of the `benchmark_output` folders for each project: - AIR-Bench: `gs://crfm-helm-public/air-bench/benchmark_output` - Lite: `gs://crfm-helm-public/lite/benchmark_output` - MMLU: `gs://crfm-helm-public/mmlu/benchmark_output` -- Classic: `gs://crfm-helm-public/benchmark_output` (see warning above) +- Classic: `gs://crfm-helm-public/classic/benchmark_output` - HEIM: `gs://crfm-helm-public/heim/benchmark_output` - Instruct: `gs://crfm-helm-public/instruct/benchmark_output` - MedHELM: `gs://crfm-helm-public/medhelm/benchmark_output` @@ -35,15 +35,15 @@ Locations of the `benchmark_output` folders for each project: ## Download a whole project -Warning: Downloading a whole HELM project requires a very large amounts of disk space - a few hundred GB for most projects, and more than 1 TB for Classic. Ensure that you have enough local disk space before downloading these projects. +Warning: Downloading a whole HELM project requires a very large amounts of disk space - a few hundred GB for most projects, and more than 5 TB for Classic. Ensure that you have enough local disk space before downloading these projects. 1. (Optional) Use the `gcloud storage du` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/du)) command to compute the size of the download and ensure you have enough space on your local disk: ```sh gcloud storage du -sh $GCS_BENCHMARK_OUTPUT_PATH ``` -2. Run `gcloud storage rsync` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/rsync)) to download the data to the folder created in the previous step: +2. Run `gcloud storage cp` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/cp)) to download the data to the folder created in the previous step: ```sh -gcloud storage rsync -r $GCS_BENCHMARK_OUTPUT_PATH $LOCAL_BENCHMARK_OUTPUT_PATH +gcloud storage cp -r $GCS_BENCHMARK_OUTPUT_PATH $LOCAL_BENCHMARK_OUTPUT_PATH ``` ## Download a specific version @@ -67,11 +67,11 @@ export RELEASE_VERSION=v1.0.0 ``` 2. Create a local directory to store the data: ```sh -mkdir $LOCAL_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION +mkdir -p $LOCAL_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION ``` -3. Run `gcloud storage rsync` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/du)) to download the data to the folder created in the previous step: +3. Run `gcloud storage cp` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/cp)) to download the data to the folder created in the previous step: ```sh -gcloud storage rsync -r $GCS_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION $LOCAL_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION +gcloud storage cp -r $GCS_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION $LOCAL_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION ``` 4. Inspect the file contents of `$LOCAL_BENCHMARK_OUTPUT_PATH/releases/$RELEASE_VERSION/summary.json`. For _each_ suite listed in the `suites` array field, repeat the steps in **Download a specific suite** for that suite. @@ -83,11 +83,11 @@ export SUITE_VERSION=v1.0.0 ``` 2. Create a local directory to store the data: ```sh -mkdir $LOCAL_BENCHMARK_OUTPUT_PATH/runs/$SUITE_VERSION +mkdir -p $LOCAL_BENCHMARK_OUTPUT_PATH/runs/$SUITE_VERSION ``` -3. Run `gcloud storage rsync` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/du)) to download the data to the folder created in the previous step: +3. Run `gcloud storage cp` ([documentation](https://cloud.google.com/sdk/gcloud/reference/storage/cp)) to download the data to the folder created in the previous step: ```sh -gcloud storage rsync -r $GCS_BENCHMARK_OUTPUT_PATH/runs/$SUITE_VERSION $LOCAL_BENCHMARK_OUTPUT_PATH/runs/$SUITE_VERSION +gcloud storage cp -r $GCS_BENCHMARK_OUTPUT_PATH/runs/$SUITE_VERSION $LOCAL_BENCHMARK_OUTPUT_PATH/runs/$SUITE_VERSION ``` ## Troubleshooting From 5fbdab8a2f47796e0498bdbd1a94e05cd6adc4a9 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Tue, 14 Apr 2026 16:10:11 -0700 Subject: [PATCH 70/89] Change Dependabot schedule interval to monthly (#4190) --- .github/dependabot.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 090670dc5cc..b3fbea0b7b7 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -3,7 +3,7 @@ updates: - package-ecosystem: "uv" directory: "/" schedule: - interval: "weekly" + interval: "monthly" ignore: - dependency-name: "*" update-types: ["version-update:semver-major"] @@ -14,7 +14,7 @@ updates: - package-ecosystem: "npm" directory: "/helm-frontend/" schedule: - interval: "weekly" + interval: "monthly" ignore: - dependency-name: "*" update-types: ["version-update:semver-major"] From b4c9d736f934bac62e6ae32238b319040bbe0c22 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Apr 2026 13:00:43 -0700 Subject: [PATCH 71/89] Bump mkdocs-include-markdown-plugin from 4.0.0 to 7.1.8 (#4163) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 2 +- uv.lock | 41 ++++++++++++++++++++++++++++++----------- 2 files changed, 31 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 409aa656ab8..4d6d8bb1e51 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -427,7 +427,7 @@ docs = [ "mkdocstrings==0.30.1", 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Subject: [PATCH 76/89] Add metadata for audio scenarios (#4185) --- .../audio_language/air_bench_chat_scenario.py | 30 +++++++++++++++++++ .../air_bench_foundation_scenario.py | 25 ++++++++++++++++ .../scenarios/audio_language/ami_scenario.py | 26 ++++++++++++++++ .../audio_language/audio_mnist_scenario.py | 21 +++++++++++++ .../audio_language/audiocaps_scenario.py | 22 ++++++++++++++ .../audio_language/corebench_scenario.py | 23 ++++++++++++++ .../audio_language/covost2_scenario.py | 25 ++++++++++++++++ .../fleurs_fairness_scenario.py | 25 ++++++++++++++++ .../audio_language/fleurs_scenario.py | 27 +++++++++++++++++ .../audio_language/iemocap_audio_scenario.py | 22 ++++++++++++++ .../librispeech_fairness_scenario.py | 26 ++++++++++++++++ .../audio_language/librispeech_scenario.py | 25 ++++++++++++++++ .../audio_language/meld_audio_scenario.py | 22 ++++++++++++++ .../multilingual_librispeech_scenario.py | 23 ++++++++++++++ .../audio_language/mustard_scenario.py | 21 +++++++++++++ .../audio_language/mutox_scenario.py | 20 +++++++++++++ .../audio_language/parade_scenario.py | 21 +++++++++++++ .../speech_robust_bench_scenario.py | 25 ++++++++++++++++ .../audio_language/vocal_sound_scenario.py | 24 +++++++++++++++ .../voice_jailbreak_attacks_scenario.py | 19 ++++++++++++ .../audio_language/voxceleb2_scenario.py | 23 ++++++++++++++ 21 files changed, 495 insertions(+) diff --git a/src/helm/benchmark/scenarios/audio_language/air_bench_chat_scenario.py b/src/helm/benchmark/scenarios/audio_language/air_bench_chat_scenario.py index 2af0ecc30b9..be33dfe39c4 100644 --- a/src/helm/benchmark/scenarios/audio_language/air_bench_chat_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/air_bench_chat_scenario.py @@ -1,6 +1,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -9,6 +10,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from helm.common.media_object import MediaObject, MultimediaObject @@ -128,3 +130,31 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=audio_meda_data_valid["answer_gt"]), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + if self._subject in ["mix", "speech"]: + group = "reasoning" + elif self._subject in ["sound", "music"]: + group = "knowledge" + else: + raise ValueError(f"Invalid subject. Valid subjects are: {AirBenchChatScenario.SUJECTS}") + return ScenarioMetadata( + name=f"air_bench_chat_{group}", + display_name=f"Air-Bench Chat ({group} subsets)", + description="Air-Bench (Yang et al, 2024) encompasses two dimensions: foundation and chat " + "benchmarks. The former consists of 19 tasks with approximately 19k " + "single-choice questions. The latter one contains 2k instances of open-ended " + "question-and-answer data. We consider the chat benchmark in this scenario.\n" + "The dataset contains the audio question answering task in four subjects: " + "sound, speech, music, and mixed. ([Yang et al, " + "2024](https://aclanthology.org/2024.acl-long.109.pdf)).\n", + taxonomy=TaxonomyInfo( + task="audio question answering", + what="audio, question, and answer of audio samples", + when="2024", + who="real speakers", + language="English", + ), + main_metric="gpt4_audio_critique", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/air_bench_foundation_scenario.py b/src/helm/benchmark/scenarios/audio_language/air_bench_foundation_scenario.py index 9a2fc12e1db..afa3698b6ef 100644 --- a/src/helm/benchmark/scenarios/audio_language/air_bench_foundation_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/air_bench_foundation_scenario.py @@ -1,6 +1,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -9,6 +10,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from helm.common.media_object import MediaObject, MultimediaObject @@ -152,3 +154,26 @@ def get_instances(self, output_path: str) -> List[Instance]: ) instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="air_bench_foundation", + display_name="Air-Bench Foundation", + description="Air-Bench (Yang et al, 2024) encompasses two dimensions: foundation and chat " + "benchmarks. The former consists of 19 tasks with approximately 19k " + "single-choice questions. The latter one contains 2k instances of open-ended " + "question-and-answer data. We consider the chat benchmark in this scenario.\n" + "The dataset contains the audio multiple choice question answering task in " + "three subjects: music instrument classification, music genera recognition, and " + "music question-answering. ([Yang et al, " + "2024](https://aclanthology.org/2024.acl-long.109.pdf)).\n", + taxonomy=TaxonomyInfo( + task="audio question answering", + what="audio, question, and answer of the question", + when="2024", + who="real speakers", + language="English", + ), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/ami_scenario.py b/src/helm/benchmark/scenarios/audio_language/ami_scenario.py index 38eacb5ac65..5dc7b7b2f02 100644 --- a/src/helm/benchmark/scenarios/audio_language/ami_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/ami_scenario.py @@ -2,6 +2,7 @@ import os import json +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from datasets import load_dataset @@ -94,3 +96,27 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="ami", + display_name="AMI Meeting Corpus", + description="The AMI Meeting Corpus (Carletta et al. 2005) is a multi-modal data set " + "consisting of 100 hours of meeting recordings. It is being created in the " + "context of a project that is developing meeting browsing technology. The " + "corpus is being recorded using a wide range of devices including " + "close-talking and far-field microphones, individual and room-view video " + "cameras, projection, a whiteboard, and individual pens, all of which produce " + "output signals that are synchronized with each other.\n" + "The dataset contains the audio, transcriptions for all subsets ([Carletta et " + "al, 2005](https://link.springer.com/chapter/10.1007/11677482_3)).\n", + taxonomy=TaxonomyInfo( + task="audio recognition", + what="audio, transcripts of audio samples from meeting environments", + when="2005", + who="real speakers", + language="English", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/audio_mnist_scenario.py b/src/helm/benchmark/scenarios/audio_language/audio_mnist_scenario.py index 4b54d82927b..3b369fc6656 100644 --- a/src/helm/benchmark/scenarios/audio_language/audio_mnist_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/audio_mnist_scenario.py @@ -3,6 +3,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -11,6 +12,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from datasets import load_dataset @@ -60,3 +62,22 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=str(row["digit"])), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="audio_mnist", + display_name="AudioMNIST", + short_display_name=None, + description="The AudioMNIST dataset consists of a dataset of 30000 audio samples of spoken " + "digits (0-9) of 60 different speakers. The task is to classify the digit from " + "the audio sample ([Becker et al, 2023](https://arxiv.org/abs/1807.03418)).\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what="audio samples of spoken digits (0-9)", + when="2018", + who="60 different speakers", + language="English", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/audiocaps_scenario.py b/src/helm/benchmark/scenarios/audio_language/audiocaps_scenario.py index 2e57280e33d..eca3eb710a7 100644 --- a/src/helm/benchmark/scenarios/audio_language/audiocaps_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/audiocaps_scenario.py @@ -1,6 +1,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -9,6 +10,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from helm.common.media_object import MediaObject, MultimediaObject @@ -57,3 +59,23 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=str(row["caption"])), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="audiocaps", + display_name="AudioCaps", + description="AudioCaps is a large-scale dataset of about 46K audio clips to human-written " + "text pairs collected via crowdsourcing on the AudioSet dataset, which covers " + "a wide range of human and animal sounds, musical instruments and genres, and " + "common everyday environmental sounds. ([Kim et al, " + "2019](https://aclanthology.org/N19-1011.pdf)).\n", + taxonomy=TaxonomyInfo( + task="audio captioning", + what="audio clips in the wild", + when="2019", + who="real speakers", + language="English", + ), + main_metric="gpt4_audio_critique", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/corebench_scenario.py b/src/helm/benchmark/scenarios/audio_language/corebench_scenario.py index e71d8650e0f..47caa0aacde 100644 --- a/src/helm/benchmark/scenarios/audio_language/corebench_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/corebench_scenario.py @@ -2,6 +2,7 @@ import os import json +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from helm.common.media_object import MediaObject, MultimediaObject @@ -75,3 +77,24 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="corebench", + display_name="COREBench", + description="The COREBench is a new audio benchmark incorporating multi-speaker " + "conversations. It consists of conversational audio, transcript, question, and " + "answer. There are two challenging features of this benchmark: (1) the " + "questions are designed to require reasoning over multiple turns of " + "conversation, and (2) the average audio length is longer than 1 minute, which " + "is significantly longer than existing benchmarks.\n", + taxonomy=TaxonomyInfo( + task="audio question-answering", + what="audio, question, transcripts and answer given the audio", + when="2025", + who="OpenAI's TTS", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/covost2_scenario.py b/src/helm/benchmark/scenarios/audio_language/covost2_scenario.py index de81060f2ce..25fd1bac3c0 100644 --- a/src/helm/benchmark/scenarios/audio_language/covost2_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/covost2_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject @@ -161,3 +163,26 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(Instance(input=input, references=references, split=split)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="covost2", + display_name="CoVost-2", + description="CoVost-2 is a large-scale multilingual speech translation corpus covering " + "translations from 21 languages into English and from English into 15 " + "languages.\n" + "The dataset contains the audio, transcriptions, and translations in the " + "following languages: French, German, Dutch, Russian, Spanish, Italian, " + "Turkish, Persian, Swedish, Mongolian, Chinese, Welsh, Catalan, Slovenian, " + "Estonian, Indonesian, Arabic, Tamil, Portuguese, Latvian, and Japanese ([Wang " + "et al, 2020](https://arxiv.org/abs/2007.10310)).\n", + taxonomy=TaxonomyInfo( + task="audio machine translation", + what="audio, transcriptions, and translations in 15 languages", + when="2020", + who="real speakers", + language="15 languages", + ), + main_metric="bleu", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/fleurs_fairness_scenario.py b/src/helm/benchmark/scenarios/audio_language/fleurs_fairness_scenario.py index d7e2a3e22fa..84ac9e62ebf 100644 --- a/src/helm/benchmark/scenarios/audio_language/fleurs_fairness_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/fleurs_fairness_scenario.py @@ -2,6 +2,7 @@ from typing import List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from collections import OrderedDict from tqdm import tqdm @@ -81,3 +83,26 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="fleurs_fairness", + display_name="FLEURS Fairness", + description="FLEURS is an n-way parallel speech dataset in 102 languages built on top of " + "the machine translation FLoRes-101 benchmark, with approximately 12 hours of " + "speech supervision per language. FLEURS can be used for a variety of speech " + "tasks, including Automatic Speech Recognition (ASR), Speech Language " + "Identification (Speech LangID), Translation and Retrieval.\n" + "We only use the English subset of the dataset for the fairness task. We ask " + "the model to do ASR on audio files from different gender groups ([Conneau et " + "al, 2022](https://arxiv.org/abs/2205.12446)).\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what="audio, transcripts, and gender of the speaker", + when="2022", + who="real speakers", + language="English", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/fleurs_scenario.py b/src/helm/benchmark/scenarios/audio_language/fleurs_scenario.py index 7307b9c807c..cad6ca3f4e4 100644 --- a/src/helm/benchmark/scenarios/audio_language/fleurs_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/fleurs_scenario.py @@ -2,6 +2,7 @@ from typing import List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from collections import OrderedDict from tqdm import tqdm @@ -310,3 +312,28 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="fleurs", + display_name="FLEURS", + description="FLEURS is an n-way parallel speech dataset in 102 languages built on top of " + "the machine translation FLoRes-101 benchmark, with approximately 12 hours of " + "speech supervision per language. FLEURS can be used for a variety of speech " + "tasks, including Automatic Speech Recognition (ASR), Speech Language " + "Identification (Speech LangID), Translation and Retrieval.\n" + "The dataset contains the audio, transcriptions, and language in 102 different " + "languages, which are divided into 7 language groups: Western European, Eastern " + "European, Central Asia Middle North African, Sub Saharan African, South " + "Asian, South East Asian, Chinese Japanase Korean ([Conneau et al, " + "2022](https://arxiv.org/abs/2205.12446)).\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what="audio, transcripts, and language names in 102 languages", + when="2022", + who="real speakers", + language="102 languages", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/iemocap_audio_scenario.py b/src/helm/benchmark/scenarios/audio_language/iemocap_audio_scenario.py index abcc9776fdd..5dd4ca802d2 100644 --- a/src/helm/benchmark/scenarios/audio_language/iemocap_audio_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/iemocap_audio_scenario.py @@ -2,6 +2,7 @@ import os from typing import List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from helm.common.audio_utils import ensure_audio_file_exists_from_array from helm.common.general import ensure_directory_exists @@ -81,3 +83,23 @@ def get_instances(self, output_path: str) -> List[Instance]: print(row["audio"]) break return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="iemocap_audio", + display_name="IEMOCAP Audio", + short_display_name=None, + description="A classification scenario based on audio data from the Interactive emotional " + "dyadic motion capture database (IEMOCAP) ([Busso et al, " + "2008](https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf)). The task is to " + "classify the emotion of the speaker(s) in the audio sample.\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what="audio recordings improvisations or scripted scenarios", + when="2007", + who="10 professional actors", + language="English", + ), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/librispeech_fairness_scenario.py b/src/helm/benchmark/scenarios/audio_language/librispeech_fairness_scenario.py index ea569c11d9a..d26b5bdc750 100644 --- a/src/helm/benchmark/scenarios/audio_language/librispeech_fairness_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/librispeech_fairness_scenario.py @@ -2,6 +2,7 @@ import os import json +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from collections import OrderedDict from tqdm import tqdm @@ -94,3 +96,27 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="librispeech_fairness", + display_name="LibriSpeech Fairness", + description="The LibriSpeech corpus (Vassil et al. 2015) is derived from audiobooks that " + "are part of the LibriVox project, and contains 1000 hours of speech sampled " + "at 16 kHz. The data has separately prepared language-model training data and " + "pre-built language models. This corpus is one of the most widely-used ASR " + "corpus, which has been extended to many applicaitons such as robust ASR and " + "multilingual ASR tasks.\n" + "The dataset contains the audio, transcriptions for all subsets. We ask the " + "model to do ASR on audio files from different gender groups ([Vassil et al. " + "2015](https://ieeexplore.ieee.org/document/7178964)).\n", + taxonomy=TaxonomyInfo( + task="audio recognition", + what="audio, transcripts of audio samples in daily scenarios", + when="2015", + who="real speakers", + language="English", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/librispeech_scenario.py b/src/helm/benchmark/scenarios/audio_language/librispeech_scenario.py index 4445ba2521b..a5de7896292 100644 --- a/src/helm/benchmark/scenarios/audio_language/librispeech_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/librispeech_scenario.py @@ -2,6 +2,7 @@ import os import json +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from datasets import load_dataset @@ -78,3 +80,26 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="librispeech", + display_name="LibriSpeech", + description="The LibriSpeech corpus (Vassil et al. 2015) is derived from audiobooks that " + "are part of the LibriVox project, and contains 1000 hours of speech sampled " + "at 16 kHz. The data has separately prepared language-model training data and " + "pre-built language models. This corpus is one of the most widely-used ASR " + "corpus, which has been extended to many applicaitons such as robust ASR and " + "multilingual ASR tasks.\n" + "The dataset contains the audio, transcriptions for all subsets ([Vassil et " + "al. 2015](https://ieeexplore.ieee.org/document/7178964)).\n", + taxonomy=TaxonomyInfo( + task="audio recognition", + what="audio, transcripts of audio samples in daily scenarios", + when="2015", + who="real speakers", + language="English", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/meld_audio_scenario.py b/src/helm/benchmark/scenarios/audio_language/meld_audio_scenario.py index dcb95cacbd7..3636ca8e52e 100644 --- a/src/helm/benchmark/scenarios/audio_language/meld_audio_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/meld_audio_scenario.py @@ -4,6 +4,7 @@ import pandas as pd from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( TEST_SPLIT, TRAIN_SPLIT, @@ -14,6 +15,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from helm.common.audio_utils import ensure_audio_file_exists_from_array, get_array_from_audio_file from helm.common.media_object import MediaObject, MultimediaObject @@ -111,3 +113,23 @@ def get_instances(self, output_path: str) -> List[Instance]: ) instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="meld_audio", + display_name="Multimodal EmotionLines Dataset (MELD) Audio", + description="Multimodal EmotionLines Dataset (MELD) has been created by enhancing and " + "extending EmotionLines dataset. MELD has more than 1400 dialogues and 13000 " + "utterances from Friends TV series. Multiple speakers participated in the " + "dialogues. Each utterance in a dialogue has been labeled by any of these seven " + "emotions - Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear.\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what="Classify audio by emotion", + when="1994-2004", # corrected from 2018 in the schema file + who="Friends TV series", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/multilingual_librispeech_scenario.py b/src/helm/benchmark/scenarios/audio_language/multilingual_librispeech_scenario.py index 38f2df3cc79..f99f37948fe 100644 --- a/src/helm/benchmark/scenarios/audio_language/multilingual_librispeech_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/multilingual_librispeech_scenario.py @@ -1,6 +1,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -9,6 +10,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from datasets import load_dataset @@ -78,3 +80,24 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=str(answer)), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="multilingual_librispeech", + display_name="Multilingual Librispeech", + description="Multilingual Librispeech is derived from read audiobooks from LibriVox and " + "consists of 8 languages, including about 44.5K hours of English and a total " + "of about 6K hours for other languages. \n" + "The dataset contains the audio and transcriptions in the following languages: " + 'Dutch, German, French, Spanish, Italian, Portuguese", Polish ([Pratap et al, ' + "2022](https://arxiv.org/abs/2012.03411)).\n", + taxonomy=TaxonomyInfo( + task="audio captioning", + what="audio and transcriptions in 8 languages", + when="2020", + who="real speakers", + language="8 languages", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/mustard_scenario.py b/src/helm/benchmark/scenarios/audio_language/mustard_scenario.py index bc740f0c09c..f15c2a0f932 100644 --- a/src/helm/benchmark/scenarios/audio_language/mustard_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/mustard_scenario.py @@ -4,6 +4,7 @@ from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( TEST_SPLIT, Scenario, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from helm.common.audio_utils import is_invalid_audio_file, extract_audio from helm.common.media_object import MediaObject, MultimediaObject @@ -140,3 +142,22 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mustard", + display_name="MUStARD", + description="A multimodal video corpus for research in automated sarcasm discovery. The " + "dataset is compiled from popular TV shows including Friends, The Golden Girls, " + "The Big Bang Theory, and Sarcasmaholics Anonymous. ([Castro et al, " + "2019](https://arxiv.org/abs/1906.01815)).\n", + taxonomy=TaxonomyInfo( + task="sarcasm detection", + what="clips from television shows", + when="2019", + who="real speakers", + language="English", + ), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/mutox_scenario.py b/src/helm/benchmark/scenarios/audio_language/mutox_scenario.py index bf3f5c2f2d9..a654026cfc1 100644 --- a/src/helm/benchmark/scenarios/audio_language/mutox_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/mutox_scenario.py @@ -3,6 +3,7 @@ import os import requests +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from pydub import AudioSegment from tqdm import tqdm import pandas as pd @@ -15,6 +16,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from helm.common.audio_utils import is_invalid_audio_file from helm.common.media_object import MediaObject, MultimediaObject @@ -252,3 +254,21 @@ def get_instances(self, output_path: str) -> List[Instance]: assert len(instances) > 0, f"No instances found for language: {self._language_code}" return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mutox", + display_name="MuTox", + description="The dataset consists of 20k audio utterances for English and Spanish, and 4k " + "for the other languages. ([Costa-jussà et al, " + "2018](https://arxiv.org/abs/2401.05060)).\n", + taxonomy=TaxonomyInfo( + task="toxicity detection", + what="samples of utterances", + when="2024", + who="real speakers", + language="30 languages", + ), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/parade_scenario.py b/src/helm/benchmark/scenarios/audio_language/parade_scenario.py index c68e77754f4..7d70c430ac0 100644 --- a/src/helm/benchmark/scenarios/audio_language/parade_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/parade_scenario.py @@ -2,6 +2,7 @@ import os import json +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -10,6 +11,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from helm.common.media_object import MediaObject, MultimediaObject @@ -95,3 +97,22 @@ def get_instances(self, output_path: str) -> List[Instance]: ) instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="parade", + display_name="PARADE", + description="The PARADE dataset is inspired by the PAIRS dataset for evaluating occupation " + "and status bias in vision-language models. We collect a new dataset of " + "audio-text multi-choice QA task that involves exploring occupation and status " + "bias. The dataset consists of 436 audio-text QA pairs with 3 options each.\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what="audio, question and answer given the audio", + when="2025", + who="OpenAI's TTS", + language="English", + ), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/speech_robust_bench_scenario.py b/src/helm/benchmark/scenarios/audio_language/speech_robust_bench_scenario.py index 056be4d5ea2..7c6c094db4e 100644 --- a/src/helm/benchmark/scenarios/audio_language/speech_robust_bench_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/speech_robust_bench_scenario.py @@ -4,6 +4,7 @@ import os import json +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from datasets import load_dataset @@ -122,3 +124,26 @@ def get_instances(self, output_path: str) -> List[Instance]: references = [Reference(Output(text=answer), tags=[CORRECT_TAG])] instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="speech_robust_bench", + display_name="Robust Speech Bench", + description="Speech Robust Bench (Shah et al, 2024) is a comprehensive benchmark for " + "evaluating the robustness of ASR models to diverse corruptions. SRB is " + "composed of 114 input perturbations which simulate an heterogeneous range of " + "corruptions that ASR models may encounter when deployed in the wild. In this " + "scenario, we select 4 subsets: accent_cv, accent_cv_es, chinme, and AIM for " + "evaluation.\n" + "The dataset contains the audio, transcriptions for all subsets ([Shah et al, " + "2024](https://arxiv.org/abs/2403.07937)).\n", + taxonomy=TaxonomyInfo( + task="audio recognition", + what="audio, transcripts of audio samples in a wide range of " "perturbations", + when="2024", + who="real speakers", + language="English, Spanish", + ), + main_metric="wer_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/vocal_sound_scenario.py b/src/helm/benchmark/scenarios/audio_language/vocal_sound_scenario.py index 2bf3999acdf..749811caa95 100644 --- a/src/helm/benchmark/scenarios/audio_language/vocal_sound_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/vocal_sound_scenario.py @@ -3,6 +3,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -11,6 +12,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm @@ -81,3 +83,25 @@ def get_instances(self, output_path: str) -> List[Instance]: assert len(instances) > 0, f"No instances found for sound: {self._sound}" return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="vocal_sound", + display_name="VocalSound", + description="VocalSound dataset consisting of over 21,000 crowdsourced recordings of " + "laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 " + "unique subjects. \n" + "Different from previous datasets, the VocalSound dataset contains meta " + "information such as speaker age, gender, native language, country, and health " + "condition ([Gong et al, 2022](https://arxiv.org/abs/2205.03433)).\n", + taxonomy=TaxonomyInfo( + task="audio classification", + what='audio samples of behaviours ("cough", "laughter", "sigh", ' + '"sneeze", "sniff", "throatclearing")', + when="2022", + who="real speakers", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/voice_jailbreak_attacks_scenario.py b/src/helm/benchmark/scenarios/audio_language/voice_jailbreak_attacks_scenario.py index bfce5814df1..8560c8ed2a1 100644 --- a/src/helm/benchmark/scenarios/audio_language/voice_jailbreak_attacks_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/voice_jailbreak_attacks_scenario.py @@ -1,11 +1,13 @@ import os from typing import List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( TEST_SPLIT, Scenario, Instance, Input, + ScenarioMetadata, ) from helm.common.audio_utils import is_invalid_audio_file from helm.common.media_object import MediaObject, MultimediaObject @@ -85,3 +87,20 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(Instance(input=input, references=[], split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="voice_jailbreak_attacks", + display_name="Voice Jailbreak Attacks Against GPT-4o", + description="Voice jailbreak attacks against GPT-4o ([Shen et al, " + "2024](https://arxiv.org/abs/2405.19103)).\n", + taxonomy=TaxonomyInfo( + task="refusal for safety", + what="voice jailbreak attacks against GPT-4o", + when="2024", + who="AI-generated speech", + language="English", + ), + main_metric="refusal_rate", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/audio_language/voxceleb2_scenario.py b/src/helm/benchmark/scenarios/audio_language/voxceleb2_scenario.py index 1a1218bfd56..0fdf6c4722f 100644 --- a/src/helm/benchmark/scenarios/audio_language/voxceleb2_scenario.py +++ b/src/helm/benchmark/scenarios/audio_language/voxceleb2_scenario.py @@ -1,6 +1,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -9,6 +10,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) from tqdm import tqdm from helm.common.media_object import MediaObject, MultimediaObject @@ -103,3 +105,24 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(Instance(input=input, references=references, split=TEST_SPLIT)) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="voxceleb2", + display_name="VoxCeleb2", + description="VoxCeleb is an audio-visual dataset consisting of short clips of human speech, " + "extracted from interview videos uploaded to YouTube. It contains over a " + "million utterances from over 6,000 speakers with their gender, race, identity " + "information in 145 different nationalities, covering a wide range of accents, " + "ages, ethnicities and languages. ([Chung et al, " + "2018](https://www.robots.ox.ac.uk/~vgg/publications/2018/Chung18a/chung18a.pdf))\n", + taxonomy=TaxonomyInfo( + task="audio identification", + what="audio clips in the wild", + when="2018", + who="real speakers", + language="English, Germany, French", + ), + main_metric="quasi_exact_match", + main_split="test", + ) From 094d12b6355c453f63a2ada9a2bb68b3d7890b42 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Apr 2026 13:14:45 -0700 Subject: [PATCH 77/89] Bump pytest from 7.2.2 to 9.0.3 (#4194) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 2 +- uv.lock | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4d6d8bb1e51..8999b3a63fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -411,7 +411,7 @@ litellm = [ [dependency-groups] dev = [ - "pytest~=7.2.0", + "pytest>=7.2,<9.1", "xdoctest~=1.2.0", "pre-commit~=2.20.0", # Errors produced by type checkers and linters are very version-specific diff --git a/uv.lock b/uv.lock index 45a5992479a..d55299b225c 100644 --- a/uv.lock +++ b/uv.lock @@ -1833,7 +1833,7 @@ dev = [ { name = "flake8", specifier = "==7.3.0" }, { name = "mypy", specifier = "==1.16.0" }, { name = "pre-commit", specifier = "~=2.20.0" }, - { name = "pytest", specifier = "~=7.2.0" }, + { name = "pytest", specifier = ">=7.2,<9.1" }, { name = "xdoctest", specifier = "~=1.2.0" }, ] docs = [ @@ -7431,20 +7431,20 @@ wheels = [ [[package]] name = "pytest" -version = "7.2.2" +version = "9.0.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "attrs" }, { name = "colorama", marker = "sys_platform == 'win32'" }, { name = "exceptiongroup", marker = "python_full_version < '3.11'" }, { name = "iniconfig" }, { name = "packaging" }, { name = "pluggy" }, + { name = "pygments" }, { name = "tomli", marker = "python_full_version < '3.11'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b9/29/311895d9cd3f003dd58e8fdea36dd895ba2da5c0c90601836f7de79f76fe/pytest-7.2.2.tar.gz", hash = "sha256:c99ab0c73aceb050f68929bc93af19ab6db0558791c6a0715723abe9d0ade9d4", size = 1320028, upload-time = "2023-03-03T19:12:29.775Z" } +sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b2/68/5321b5793bd506961bd40bdbdd0674e7de4fb873ee7cab33dd27283ad513/pytest-7.2.2-py3-none-any.whl", hash = "sha256:130328f552dcfac0b1cec75c12e3f005619dc5f874f0a06e8ff7263f0ee6225e", size = 317207, upload-time = "2023-03-03T19:12:27.611Z" }, + { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" }, ] [[package]] From 5dede25d4bedeaf229656cdb3cf5d1bf6d16af68 Mon Sep 17 00:00:00 2001 From: Chessing234 <134995372+Chessing234@users.noreply.github.com> Date: Thu, 16 Apr 2026 01:45:20 +0530 Subject: [PATCH 78/89] Fix _apply_output_mapping_pattern returning wrong match results (#4192) Co-authored-by: Claude Opus 4.6 (1M context) --- src/helm/benchmark/metrics/evaluate_reference_metrics.py | 4 ++-- src/helm/benchmark/presentation/run_display.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/helm/benchmark/metrics/evaluate_reference_metrics.py b/src/helm/benchmark/metrics/evaluate_reference_metrics.py index b67d1d96369..049ebc38366 100644 --- a/src/helm/benchmark/metrics/evaluate_reference_metrics.py +++ b/src/helm/benchmark/metrics/evaluate_reference_metrics.py @@ -402,9 +402,9 @@ def _apply_output_mapping_pattern(pattern: str, prediction: str) -> str: if not match: return "" elif match.groups(): - return match.group(0) + return match.group(1) else: - return match.string + return match.group(0) # TODO This should probably be made into an implementation of MetricInterface. For now it lives here diff --git a/src/helm/benchmark/presentation/run_display.py b/src/helm/benchmark/presentation/run_display.py index 2c9c778cdb2..156d662c561 100644 --- a/src/helm/benchmark/presentation/run_display.py +++ b/src/helm/benchmark/presentation/run_display.py @@ -271,9 +271,9 @@ def write_run_display_json(run_path: str, run_spec: RunSpec, schema: Schema, ski if not match: output_to_map = "" elif match.groups(): - output_to_map = match.group(0) + output_to_map = match.group(1) else: - output_to_map = match.string + output_to_map = match.group(0) mapped_output = request_state.output_mapping.get(output_to_map) instance_id_to_instance[(request_state.instance.id, request_state.instance.perturbation)] = ( request_state.instance From bfd9b9636e541f9c34fdba4e651b8e1c1cf17757 Mon Sep 17 00:00:00 2001 From: Chessing234 <134995372+Chessing234@users.noreply.github.com> Date: Thu, 16 Apr 2026 01:45:32 +0530 Subject: [PATCH 79/89] Add link to per_instance_stats.json on frontend (#4166) Co-authored-by: Claude Opus 4.6 (1M context) --- helm-frontend/src/routes/Run.tsx | 9 +++++++++ .../src/services/getPerInstanceStatsByName.ts | 11 +++++++++++ 2 files changed, 20 insertions(+) create mode 100644 helm-frontend/src/services/getPerInstanceStatsByName.ts diff --git a/helm-frontend/src/routes/Run.tsx b/helm-frontend/src/routes/Run.tsx index 3094a855d91..64ac69fe647 100644 --- a/helm-frontend/src/routes/Run.tsx +++ b/helm-frontend/src/routes/Run.tsx @@ -14,6 +14,7 @@ import type MetricFieldMap from "@/types/MetricFieldMap"; import getRunSpecByName, { getRunSpecByNameUrl, } from "@/services/getRunSpecByName"; +import { getPerInstanceStatsByNameUrl } from "@/services/getPerInstanceStatsByName"; import { getScenarioStateByNameUrl } from "@/services/getScenarioStateByName"; import Tab from "@/components/Tab"; import Tabs from "@/components/Tabs"; @@ -164,6 +165,14 @@ export default function Run() { > Full JSON + + Per-Instance Stats JSON +
diff --git a/helm-frontend/src/services/getPerInstanceStatsByName.ts b/helm-frontend/src/services/getPerInstanceStatsByName.ts new file mode 100644 index 00000000000..654c4c90cb0 --- /dev/null +++ b/helm-frontend/src/services/getPerInstanceStatsByName.ts @@ -0,0 +1,11 @@ +import getBenchmarkEndpoint from "@/utils/getBenchmarkEndpoint"; +import getBenchmarkSuite from "@/utils/getBenchmarkSuite"; + +export function getPerInstanceStatsByNameUrl( + runName: string, + suite?: string, +): string { + return getBenchmarkEndpoint( + `/runs/${suite || getBenchmarkSuite()}/${runName}/per_instance_stats.json`, + ); +} From d4ba787c96256fe1ee4d95789fcd2be053139c58 Mon Sep 17 00:00:00 2001 From: Chessing234 <134995372+Chessing234@users.noreply.github.com> Date: Thu, 16 Apr 2026 01:45:48 +0530 Subject: [PATCH 80/89] Fix print_summary() showing relevance scores under COHERENCE (#4183) Co-authored-by: Claude Opus 4.6 (1M context) --- src/helm/benchmark/metrics/summarization_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/helm/benchmark/metrics/summarization_metrics.py b/src/helm/benchmark/metrics/summarization_metrics.py index 6c4d0d5465d..2d1c3a0b6f1 100644 --- a/src/helm/benchmark/metrics/summarization_metrics.py +++ b/src/helm/benchmark/metrics/summarization_metrics.py @@ -493,7 +493,7 @@ def print_summary(self): print("=" * 40) print("COHERENCE") - for model, score in self._compute_average(self.relevance): + for model, score in self._compute_average(self.coherence): print(f"{model:40}: {score:.4f}") print("=" * 40) From 10a23d27474e2c8c36155a9fd442140a33003455 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 15 Apr 2026 13:16:01 -0700 Subject: [PATCH 81/89] Add metadata for VHELM scenarios (#4189) --- .../vision_language/a_okvqa_scenario.py | 20 ++++++++++++++ .../vision_language/bingo_scenario.py | 20 ++++++++++++++ .../vision_language/blink_scenario.py | 20 ++++++++++++++ .../crossmodal_3600_scenario.py | 20 ++++++++++++++ .../vision_language/exams_v_scenario.py | 21 +++++++++++++++ .../vision_language/fair_face_scenario.py | 19 +++++++++++++ .../vision_language/flickr30k_scenario.py | 16 +++++++++++ .../scenarios/vision_language/gqa_scenario.py | 19 +++++++++++++ .../vision_language/hateful_memes_scenario.py | 15 +++++++++++ .../vision_language/math_vista_scenario.py | 19 +++++++++++++ .../vision_language/mementos_scenario.py | 27 ++++++++++++------- .../mm_safety_bench_scenario.py | 19 +++++++++++++ .../vision_language/mm_star_scenario.py | 20 ++++++++++++++ .../scenarios/vision_language/mme_scenario.py | 20 ++++++++++++++ .../vision_language/mmmu_scenario.py | 21 +++++++++++++++ .../vision_language/msr_vtt_scenario.py | 15 +++++++++++ .../vision_language/pairs_scenario.py | 19 +++++++++++++ .../vision_language/pope_scenario.py | 20 ++++++++++++++ .../vision_language/real_world_qa_scenario.py | 19 +++++++++++++ .../vision_language/seed_bench_scenario.py | 20 ++++++++++++++ .../vision_language/unicorn_scenario.py | 19 +++++++++++++ .../vision_language/vibe_eval_scenario.py | 20 ++++++++++++++ .../vision_language/viz_wiz_scenario.py | 20 ++++++++++++++ .../scenarios/vision_language/vqa_scenario.py | 19 +++++++++++++ 24 files changed, 458 insertions(+), 9 deletions(-) diff --git a/src/helm/benchmark/scenarios/vision_language/a_okvqa_scenario.py b/src/helm/benchmark/scenarios/vision_language/a_okvqa_scenario.py index c2fe2a85691..2e754823d9a 100644 --- a/src/helm/benchmark/scenarios/vision_language/a_okvqa_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/a_okvqa_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, ALL_SPLITS, @@ -14,6 +15,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -81,3 +83,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="a_okvqa", + display_name="A-OKVQA", + description="A crowdsourced dataset composed of a diverse set of about 25K questions " + "requiring a broad base of commonsense and world knowledge to answer ([Schwenk " + "et al., 2022](https://arxiv.org/abs/2206.01718)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Real-world images", + when="2023", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/bingo_scenario.py b/src/helm/benchmark/scenarios/vision_language/bingo_scenario.py index fdfffca35c2..9a9cb33ea5a 100644 --- a/src/helm/benchmark/scenarios/vision_language/bingo_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/bingo_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists, ensure_file_downloaded @@ -101,3 +103,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="bingo", + display_name="Bingo", + description="Open-ended questions about biased images and hallucinations-inducing images " + "([Cui et al., 2023](https://arxiv.org/abs/2311.03287)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Biased images about Region, OCR, Factual, Text-to-Image and " + "Image-to-Image inference challenges", + when="2023", + who="Human experts", + language="English, Chinese, Japanese, etc.", + ), + main_metric="prometheus_vision", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/blink_scenario.py b/src/helm/benchmark/scenarios/vision_language/blink_scenario.py index 60f8ecd1d82..c164e4fa0a3 100644 --- a/src/helm/benchmark/scenarios/vision_language/blink_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/blink_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, VALID_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.images_utils import generate_hash @@ -138,3 +140,21 @@ def get_image_header(image_index: int) -> str: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="blink", + display_name="BLINK", + description="BLINK is a benchmark containing 14 visual perception tasks that can be solved " + "by humans within a blink, but difficulty for VLMs. ([Fu, " + "2024](https://arxiv.org/abs/2404.12390)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Real-world images", + when="2024", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/crossmodal_3600_scenario.py b/src/helm/benchmark/scenarios/vision_language/crossmodal_3600_scenario.py index f969cd5cca8..878daee6965 100644 --- a/src/helm/benchmark/scenarios/vision_language/crossmodal_3600_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/crossmodal_3600_scenario.py @@ -2,6 +2,7 @@ import os from typing import Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -10,6 +11,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_file_downloaded @@ -133,3 +135,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="crossmodal_3600", + display_name="Crossmodal 3600", + description="Crossmodal-3600 dataset (XM3600 in short), a geographically-diverse set of " + "3600 images annotated with human-generated reference captions in 36 languages. " + "([Thapliyal et al., 2022](https://arxiv.org/abs/2205.12522))", + taxonomy=TaxonomyInfo( + task="multilingual captioning", + what="Real-world images", + when="2022", + who="Human experts", + language="36 languages", + ), + main_metric="prometheus_vision", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/exams_v_scenario.py b/src/helm/benchmark/scenarios/vision_language/exams_v_scenario.py index 8c974d722b2..a6590d6843a 100644 --- a/src/helm/benchmark/scenarios/vision_language/exams_v_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/exams_v_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -13,6 +14,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.images_utils import generate_hash @@ -102,3 +104,22 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="exams_v", + display_name="Exams-V", + description="A multimodal and multilingual benchmark with knowledge-intensive exam " + "questions covering natural science, social science, and other miscellaneous " + "studies ([Das et al., 2024]( https://arxiv.org/abs/2403.10378)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Exam questions", + when="2024", + who="Human experts", + language="English, Chinese, Croation, Hungarian, Arabic, Serbian, " + "Bulgarian, English, German, French, Spanish, Polish", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/fair_face_scenario.py b/src/helm/benchmark/scenarios/vision_language/fair_face_scenario.py index 83fa4a0817d..e6fab2cd53f 100644 --- a/src/helm/benchmark/scenarios/vision_language/fair_face_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/fair_face_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, VALID_SPLIT, @@ -13,6 +14,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.images_utils import generate_hash @@ -134,3 +136,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="fair_face", + display_name="FairFace", + description="Identify the race, gender or age of a photo of a person ([Karkkainen et al., " + "2019](https://arxiv.org/abs/1908.04913)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Fairness", + when="2019", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/flickr30k_scenario.py b/src/helm/benchmark/scenarios/vision_language/flickr30k_scenario.py index adef5001b01..a04be8324b1 100644 --- a/src/helm/benchmark/scenarios/vision_language/flickr30k_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/flickr30k_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, VALID_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -72,3 +74,17 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="flickr30k", + display_name="Flickr30k", + description="An image caption corpus consisting of 158,915 crowd-sourced captions " + "describing 31,783 Flickr images. ([Young et al., " + "2014](https://shannon.cs.illinois.edu/DenotationGraph/TACLDenotationGraph.pdf))", + taxonomy=TaxonomyInfo( + task="image captioning", what="Flickr images", when="2014", who="Human experts", language="English" + ), + main_metric="prometheus_vision", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/gqa_scenario.py b/src/helm/benchmark/scenarios/vision_language/gqa_scenario.py index 752b531cce6..2d6160a55e0 100644 --- a/src/helm/benchmark/scenarios/vision_language/gqa_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/gqa_scenario.py @@ -2,6 +2,7 @@ import json from typing import Any, Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( ALL_SPLITS, CORRECT_TAG, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_file_downloaded @@ -89,3 +91,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="gqa", + display_name="GQA", + description="Questions about real-world visual reasoning and compositional QA ([Hudson and " + "Manning, 2019](https://arxiv.org/abs/1902.09506)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Real-world images", + when="2019", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/hateful_memes_scenario.py b/src/helm/benchmark/scenarios/vision_language/hateful_memes_scenario.py index efb7cc02de2..25b6e379e1a 100644 --- a/src/helm/benchmark/scenarios/vision_language/hateful_memes_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/hateful_memes_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( ALL_SPLITS, CORRECT_TAG, @@ -13,6 +14,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists, ensure_file_downloaded @@ -92,3 +94,16 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="hateful_memes", + display_name="Hateful Memes", + description="Multimodal hate speech detection in memes ([Kiela et al., " + "2020](https://arxiv.org/abs/2005.04790)).", + taxonomy=TaxonomyInfo( + task="toxicity identification", what="Memes", when="2020", who="Human experts", language="English" + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/math_vista_scenario.py b/src/helm/benchmark/scenarios/vision_language/math_vista_scenario.py index 2a29f44b2c0..2b3ad5f7fe1 100644 --- a/src/helm/benchmark/scenarios/vision_language/math_vista_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/math_vista_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -115,3 +117,20 @@ def get_instances(self, output_path: str) -> List[Instance]: len(instances) > 0 ), f"No instances found for subject {self._grade} and question type {self._question_type}" return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="math_vista", + display_name="MathVista", + description="A benchmark designed to combine challenges from diverse mathematical and " + "visual tasks ([Lu et al., 2024](https://arxiv.org/abs/2310.02255)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Evaluating Math Reasoning in Visual Contexts", + when="2024", + who="Human experts", + language="English", + ), + main_metric="exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/mementos_scenario.py b/src/helm/benchmark/scenarios/vision_language/mementos_scenario.py index 1f8dac3dcca..cd333d162e5 100644 --- a/src/helm/benchmark/scenarios/vision_language/mementos_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/mementos_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -13,6 +14,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.general import ensure_directory_exists, ensure_file_downloaded from helm.common.media_object import MediaObject, MultimediaObject @@ -113,12 +115,19 @@ def get_instances(self, output_path: str) -> List[Instance]: return instances - -def main(): - scenario = MementosScenario("robotics") - instances = scenario.get_instances("output") - print(instances) - - -if __name__ == "__main__": - main() + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mementos", + display_name="Mementos", + description="A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over " + "Image Sequences ([Wang et al., 2024](https://arxiv.org/abs/2401.10529)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Image sequences of comics, daily life and robotics", + when="2024", + who="Human experts", + language="English", + ), + main_metric="prometheus_vision", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/mm_safety_bench_scenario.py b/src/helm/benchmark/scenarios/vision_language/mm_safety_bench_scenario.py index ddd5c746564..6c4535f3a64 100644 --- a/src/helm/benchmark/scenarios/vision_language/mm_safety_bench_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/mm_safety_bench_scenario.py @@ -2,11 +2,13 @@ import json from typing import Any, Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( TEST_SPLIT, Instance, Input, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_file_downloaded @@ -101,3 +103,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mm_safety_bench", + display_name="MM-SafetyBench", + description="Exposes the vulnerability of open-source VLMs with toxic and biased content " + "([Liu et al., 2023](https://arxiv.org/abs/2311.17600)).", + taxonomy=TaxonomyInfo( + task="toxicity mitigation", + what="Jail-break images", + when="2023", + who="Human experts", + language="English", + ), + main_metric="toxic_frac", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/mm_star_scenario.py b/src/helm/benchmark/scenarios/vision_language/mm_star_scenario.py index d34982dc527..4cdc0901a18 100644 --- a/src/helm/benchmark/scenarios/vision_language/mm_star_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/mm_star_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, VALID_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.images_utils import generate_hash @@ -93,3 +95,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mm_star", + display_name="MM-Star", + description="MM-STAR is an elite vision-indispensable multi-modal benchmark comprising " + "1,500 challenge samples meticulously selected by humans. ([Chen et al., " + "2024](https://arxiv.org/abs/2403.20330)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Real-world images", + when="2024", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/mme_scenario.py b/src/helm/benchmark/scenarios/vision_language/mme_scenario.py index e78488480c7..c620da70be2 100644 --- a/src/helm/benchmark/scenarios/vision_language/mme_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/mme_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -146,3 +148,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mme", + display_name="MME", + description="A comprehensive MLLM Evaluation benchmark with perception and cognition " + "evaluations on 14 subtasks ([Fu et al., " + "2023](https://arxiv.org/abs/2306.13394)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Real-world images", + when="2023", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/mmmu_scenario.py b/src/helm/benchmark/scenarios/vision_language/mmmu_scenario.py index 4fa77957f9c..bced5099dc5 100644 --- a/src/helm/benchmark/scenarios/vision_language/mmmu_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/mmmu_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, VALID_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -185,3 +187,22 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="mmmu", + display_name="MMMU", + description="A benchmark designed to evaluate multimodal models on massive multi-discipline " + "tasks demanding college-level subject knowledge and deliberate reasoning ([Yue " + "et al., 2023](https://arxiv.org/abs/2311.16502)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Art & Design, Business, Science, Health & Medicine, Humanities " + "& Social Science, and Tech & Engineering", + when="2023", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/msr_vtt_scenario.py b/src/helm/benchmark/scenarios/vision_language/msr_vtt_scenario.py index 307766f77a7..586290d03df 100644 --- a/src/helm/benchmark/scenarios/vision_language/msr_vtt_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/msr_vtt_scenario.py @@ -3,6 +3,7 @@ import json import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -11,6 +12,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_file_downloaded @@ -73,3 +75,16 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="msr_vtt", + display_name="MST-VTT", + description="A large-scale video benchmark for video understanding, especially the emerging " + "task of translating video to text.", + taxonomy=TaxonomyInfo( + task="captioning", what="Real-world videos", when="2016", who="Human experts", language="English" + ), + main_metric="f1_score", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/pairs_scenario.py b/src/helm/benchmark/scenarios/vision_language/pairs_scenario.py index 95660bdc6d6..d6215c30e84 100644 --- a/src/helm/benchmark/scenarios/vision_language/pairs_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/pairs_scenario.py @@ -2,6 +2,7 @@ from dataclasses import dataclass from typing import Dict, List +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -10,6 +11,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists, ensure_file_downloaded @@ -245,3 +247,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="pairs", + display_name="PAIRS", + description="Examining gender and racial bias using parallel images ([Fraser et al., " + "2024](https://arxiv.org/abs/2402.05779)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Bias", + when="2024", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/pope_scenario.py b/src/helm/benchmark/scenarios/vision_language/pope_scenario.py index 0985b85af23..eb0beac2ed5 100644 --- a/src/helm/benchmark/scenarios/vision_language/pope_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/pope_scenario.py @@ -1,6 +1,7 @@ from typing import List import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -9,6 +10,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from datasets import load_dataset from tqdm import tqdm @@ -103,3 +105,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="pope", + display_name="POPE", + description="Open-ended questions about object appearance in real-world images for " + "evaluating hallucination behaviour ([Li et al., " + "2023](https://aclanthology.org/2023.emnlp-main.20)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Real-world images", + when="2023", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/real_world_qa_scenario.py b/src/helm/benchmark/scenarios/vision_language/real_world_qa_scenario.py index 2814468919d..146517a3bbc 100644 --- a/src/helm/benchmark/scenarios/vision_language/real_world_qa_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/real_world_qa_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.images_utils import generate_hash @@ -55,3 +57,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="real_world_qa", + display_name="RealWorldQA", + description="A benchmark designed to to evaluate real-world spatial understanding " + "capabilities of multimodal models ([xAI, 2024](https://x.ai/blog/grok-1.5v)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Real world images", + when="2024", + who="Human experts", + language="English", + ), + main_metric="quasi_leave_articles_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/seed_bench_scenario.py b/src/helm/benchmark/scenarios/vision_language/seed_bench_scenario.py index d3e19293555..c1c38b14d7e 100644 --- a/src/helm/benchmark/scenarios/vision_language/seed_bench_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/seed_bench_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -129,3 +131,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="seed_bench", + display_name="Seed Bench", + description="A massive multiple-choice question-answering benchmark that spans 9 evaluation " + "aspects with the image input including the comprehension of both the image and " + "video modality ([Li et al., 2023](https://arxiv.org/abs/2307.16125)).", + taxonomy=TaxonomyInfo( + task="multiple-choice question answering", + what="Real-world images", + when="2023", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/unicorn_scenario.py b/src/helm/benchmark/scenarios/vision_language/unicorn_scenario.py index ab4b07ebcf4..f72e8c68b17 100644 --- a/src/helm/benchmark/scenarios/vision_language/unicorn_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/unicorn_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists, ensure_file_downloaded @@ -106,3 +108,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="unicorn", + display_name="Unicorn", + description="Safety Evaluation Benchmark for Evaluating on Out-of-Distribution and Sketch " + "Images ([Tu et al., 2023](https://arxiv.org/abs/2311.16101)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="OOD images and sketch images", + when="2023", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/vibe_eval_scenario.py b/src/helm/benchmark/scenarios/vision_language/vibe_eval_scenario.py index be9ae5e76b1..dcfae57eb33 100644 --- a/src/helm/benchmark/scenarios/vision_language/vibe_eval_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/vibe_eval_scenario.py @@ -4,6 +4,7 @@ from datasets import load_dataset from tqdm import tqdm +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists @@ -96,3 +98,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="vibe_eval", + display_name="Vibe Eval", + description="A difficult evaluation suite for measuring progress of multimodal language " + "models with day-to-day tasks ([Padlewski et al., " + "2024](https://arxiv.org/abs/2405.02287)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Knowledge intensive", + when="2024", + who="Human experts", + language="English", + ), + main_metric="prometheus_vision", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/viz_wiz_scenario.py b/src/helm/benchmark/scenarios/vision_language/viz_wiz_scenario.py index f86b1f20ed9..0e11d455b60 100644 --- a/src/helm/benchmark/scenarios/vision_language/viz_wiz_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/viz_wiz_scenario.py @@ -2,6 +2,7 @@ import json import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TRAIN_SPLIT, @@ -11,6 +12,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_file_downloaded @@ -105,3 +107,21 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="viz_wiz", + display_name="VizWiz", + description="A benchmark for visual question answering with images and questions created by " + "visually impaired people ([Gurari et al., " + "2018](https://arxiv.org/abs/1802.08218)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Real-world images", + when="2018", + who="Visually impaired people", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/vision_language/vqa_scenario.py b/src/helm/benchmark/scenarios/vision_language/vqa_scenario.py index 409fc6cf765..800c17430f8 100644 --- a/src/helm/benchmark/scenarios/vision_language/vqa_scenario.py +++ b/src/helm/benchmark/scenarios/vision_language/vqa_scenario.py @@ -2,6 +2,7 @@ import json import os +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( CORRECT_TAG, TEST_SPLIT, @@ -12,6 +13,7 @@ Output, Reference, Scenario, + ScenarioMetadata, ) from helm.common.media_object import MediaObject, MultimediaObject from helm.common.general import ensure_directory_exists, ensure_file_downloaded @@ -125,3 +127,20 @@ def get_instances(self, output_path: str) -> List[Instance]: ) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="vqa", + display_name="VQAv2", + description="Open-ended questions about real-world images ([Goyal et al., " + "2017](https://arxiv.org/abs/1612.00837)).", + taxonomy=TaxonomyInfo( + task="short-answer question answering", + what="Real-world images", + when="2017", + who="Human experts", + language="English", + ), + main_metric="quasi_prefix_exact_match", + main_split="valid", + ) From 36e3c5c9950d5fdb87c92c4fe27a3896e229e4f3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Apr 2026 13:24:40 -0700 Subject: [PATCH 82/89] Bump the npm group across 1 directory with 4 updates (#4200) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- helm-frontend/package.json | 8 ++-- helm-frontend/yarn.lock | 98 +++++++++++++++++++------------------- 2 files changed, 53 insertions(+), 53 deletions(-) diff --git a/helm-frontend/package.json b/helm-frontend/package.json index 39b8c2dde07..401d9f42997 100644 --- a/helm-frontend/package.json +++ b/helm-frontend/package.json @@ -31,14 +31,14 @@ "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^14.3.1", "@testing-library/user-event": "^14.6.1", - "@types/node": "^25.5.0", + "@types/node": "^25.6.0", "@types/react": "^18.2.15", "@types/react-dom": "^18.2.7", "@types/react-helmet": "^6.1.11", "@typescript-eslint/eslint-plugin": "^6.21.0", "@typescript-eslint/parser": "^6.21.0", "@vitejs/plugin-react": "^4.7.0", - "autoprefixer": "^10.4.27", + "autoprefixer": "^10.5.0", "daisyui": "^3.9.4", "eslint": "^8.45.0", "eslint-config-prettier": "^9.1.2", @@ -46,8 +46,8 @@ "eslint-plugin-react-refresh": "^0.4.26", "jest": "^29.6.1", "jsdom": "^22.1.0", - "postcss": "^8.5.8", - "prettier": "^3.8.1", + "postcss": "^8.5.10", + "prettier": "^3.8.3", "tailwindcss": "^3.4.19", "typescript": "^5.9.3", "vite": "^6.4.2", diff --git a/helm-frontend/yarn.lock b/helm-frontend/yarn.lock index 64d86089678..9cb75cd1453 100644 --- a/helm-frontend/yarn.lock +++ b/helm-frontend/yarn.lock @@ -1508,12 +1508,12 @@ resolved "https://registry.yarnpkg.com/@types/ms/-/ms-0.7.32.tgz#f6cd08939ae3ad886fcc92ef7f0109dacddf61ab" integrity sha512-xPSg0jm4mqgEkNhowKgZFBNtwoEwF6gJ4Dhww+GFpm3IgtNseHQZ5IqdNwnquZEoANxyDAKDRAdVo4Z72VvD/g== -"@types/node@*", "@types/node@^25.5.0": - version "25.5.0" - resolved "https://registry.yarnpkg.com/@types/node/-/node-25.5.0.tgz#5c99f37c443d9ccc4985866913f1ed364217da31" - integrity sha512-jp2P3tQMSxWugkCUKLRPVUpGaL5MVFwF8RDuSRztfwgN1wmqJeMSbKlnEtQqU8UrhTmzEmZdu2I6v2dpp7XIxw== +"@types/node@*", "@types/node@^25.6.0": + version "25.6.0" + resolved "https://registry.yarnpkg.com/@types/node/-/node-25.6.0.tgz#4e09bad9b469871f2d0f68140198cbd714f4edca" + integrity sha512-+qIYRKdNYJwY3vRCZMdJbPLJAtGjQBudzZzdzwQYkEPQd+PJGixUL5QfvCLDaULoLv+RhT3LDkwEfKaAkgSmNQ== dependencies: - undici-types "~7.18.0" + undici-types "~7.19.0" "@types/prop-types@*", "@types/prop-types@^15.0.0": version "15.7.8" @@ -1858,13 +1858,13 @@ asynckit@^0.4.0: resolved 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browserslist@^4.28.1: - version "4.28.1" - resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.28.1.tgz#7f534594628c53c63101079e27e40de490456a95" - integrity sha512-ZC5Bd0LgJXgwGqUknZY/vkUQ04r8NXnJZ3yYi4vDmSiZmC/pdSN0NbNRPxZpbtO4uAfDUAFffO8IZoM3Gj8IkA== +browserslist@^4.24.0, browserslist@^4.28.2: + version "4.28.2" + resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.28.2.tgz#f50b65362ef48974ca9f50b3680566d786b811d2" + integrity sha512-48xSriZYYg+8qXna9kwqjIVzuQxi+KYWp2+5nCYnYKPTr0LvD89Jqk2Or5ogxz0NUMfIjhh2lIUX/LyX9B4oIg== dependencies: - baseline-browser-mapping "^2.9.0" - caniuse-lite "^1.0.30001759" - electron-to-chromium "^1.5.263" - node-releases "^2.0.27" - update-browserslist-db "^1.2.0" + baseline-browser-mapping "^2.10.12" + caniuse-lite "^1.0.30001782" + electron-to-chromium "^1.5.328" + node-releases "^2.0.36" + update-browserslist-db "^1.2.3" bser@2.1.1: version "2.1.1" @@ -2040,10 +2040,10 @@ camelcase@^6.2.0: resolved 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Furthermore, they differ in their evaluation procedures and the scope of the evaluation, making it difficult to compare models. To address these issues, we extend the HELM framework to VLMs to present the Holistic Evaluation of Vision Language Models (VHELM). To address these issues, we introduce VHELM, built on HELM for language models. VHELM aggregates various datasets to cover one or more of the 9 aspects:"," ",e.jsx("b",{children:"visual perception"}),", ",e.jsx("b",{children:"bias"}),", ",e.jsx("b",{children:"fairness"}),", ",e.jsx("b",{children:"knowledge"}),", ",e.jsx("b",{children:"multilinguality"}),", ",e.jsx("b",{children:"reasoning"}),", ",e.jsx("b",{children:"robustness"}),","," ",e.jsx("b",{children:"safety"}),", and ",e.jsx("b",{children:"toxicity"}),". In doing so, we produce a comprehensive, multi-dimensional view of the capabilities of the VLMs across these important factors. 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We create a pipeline that downloads fresh, user-submitted data from active online communities upon execution, evaluates the VLMs shortly, and produces a leaderboard."}),e.jsx("br",{}),e.jsx("img",{src:Ct,alt:"Evaluation flowchart",className:"mx-auto block w-full",sizes:"100vw"}),e.jsx("br",{}),e.jsx("p",{children:"We introduce 3 tasks:"}),e.jsxs("ul",{className:"my-2 list-disc list-inside",children:[e.jsx("li",{children:"LaTex: equations, tables, plots and algorithms from ArXiV papers."}),e.jsx("li",{children:"Webpages: pages from GitHub written in HTML, CSS and Javascript. ..."}),e.jsx("li",{children:"Music sheets: crops of measures from IMSLP music sheets."})]}),e.jsx("div",{className:"flex flex-row justify-center mt-8",children:e.jsx("a",{className:"px-10 btn rounded-md",href:"#/leaderboard",children:"Full Leaderboard"})})]}),e.jsx("div",{className:"flex-1",children:e.jsx(E,{numRowsToDisplay:12})})]}),e.jsx("br",{}),e.jsxs("div",{className:"flex flex-col lg:flex-row items-center gap-8",children:[e.jsxs("div",{className:"flex-1 text-xl",children:[e.jsx("p",{children:"We provide an automated process for collecting new fresh data from online communities, evaluating the models and producing a leaderboard. The pipeline is designed to be executed on a regular basis to keep the leaderboard up-to-date."}),e.jsx("br",{}),e.jsxs("p",{children:["In addition to the automated data collection, we also provide a"," ",e.jsx("i",{children:"wild"})," subset for the LaTeX and webpage tasks that are collected from Wikipedia and various popular websites. These instances do not have a corresponding code, and the evaluation is done by our proposed metric: block EMD (Earth Mover Distance)."]})]}),e.jsx("div",{className:"flex-1",children:e.jsx("img",{src:_t,alt:"7 scenarios, 4 models, 4 evaluators and 5 criteria",className:"mx-auto block w-full",sizes:"200vw"})})]})]})}function Pt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"Elements of World Knowledge (EWoK)"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{children:["We present the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arxiv.org/abs/2405.09605",children:"Elements of World Knowledge (EWoK)"})," ","leaderboard in collaboration with the EWoK team. EWoK is a benchmark for evaluating world modeling in language models by testing their ability to use knowledge of a concept to match a target text with a plausible/implausible context. EWoK targets specific concepts from multiple knowledge domains known to be vital for world modeling in humans, including social interactions and spatial relations."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2405.09605",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const It=""+new URL("medhelm-overview-CND0EIsy.png",import.meta.url).href;function Ht(){const s=e.jsx("strong",{className:"font-bold",children:"MedHELM"});return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"MedHELM"}),e.jsx("p",{className:"text-xl my-4 italic text-center",children:"Holistic Evaluation of Large Language Models for Medical Tasks"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[7] text-l",children:[e.jsxs("p",{className:"my-",children:["We introduce ",s,", an extensible evaluation framework for assessing LLM performance for medical tasks. Building on the HELM framework, ",s," comprises a structured taxonomy with 5 categories, 22 subcategories, and 121 distinct clinical tasks as well as 35 distinct benchmarks (14 private, 7 gated-access, and 14 public). The benchmarks represent a spectrum of healthcare scenarios, from diagnostic decision-making to patient communication, providing a more nuanced and medically relevant assessment of AI capabilities in healthcare settings."]}),e.jsx("img",{src:It,alt:"MedHELM Task Categories",className:"mx-auto my-4 block w-7/8",sizes:"100vw"}),e.jsxs("p",{className:"my-4",children:[s," establishes a foundation for testing and evaluation of the real-world applicability of language models in healthcare. It is made possible by a unique collaboration between the Center for Research on Foundation Models, Technology and Digital Solutions at Stanford Healthcare, and Microsoft Healthcare and Life Sciences in partnership with faculty in the Departments of Medicine, Computer Science, Anesthesiology, Dermatology, Pediatrics and Biomedical Data Science as well as trainees from the MCiM program at the Clinical Excellence Research Center. The effort is coordinated by the Center for Biomedical Informatics Research."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://www.nature.com/articles/s41591-025-04151-2",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm-helm.readthedocs.io/en/latest/medhelm/",children:"Documentation"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://hai.stanford.edu/news/holistic-evaluation-of-large-language-models-for-medical-applications",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[3] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const $t=""+new URL("medhelm-v1-overview-Cu2tphBB.png",import.meta.url).href;function Dt(){const s=e.jsx("strong",{className:"font-bold",children:"MedHELM"});return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"MedHELM"}),e.jsx("p",{className:"text-xl my-4 italic text-center",children:"Medical and AI experts build a benchmark for evaluation of LLMs grounded in real-world healthcare needs."}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"my-4",children:["We introduce ",s,", a comprehensive healthcare benchmark to evaluate language models on real-world clinical tasks using real electronic health records. Building on the HELM framework,"," ",s," comprises a structured taxonomy with 5 categories, 22 subcategories, and 121 distinct clinical tasks as well as 31 diverse datasets (12 private, 6 gated-access, and 13 public). The datasets represent a spectrum of healthcare scenarios, from diagnostic decision-making to patient communication, providing a more nuanced and clinically relevant assessment of AI capabilities in healthcare settings."]}),e.jsx("img",{src:$t,alt:"MedHELM Task Categories",className:"mx-auto my-4 block w-7/8",sizes:"100vw"}),e.jsxs("p",{className:"my-4",children:[s," establishes a foundation for testing and evaluation of the real-world applicability of language models in healthcare. It is made possible by a unique collaboration between the Center for Research on Foundation Models, Technology and Digital Solutions at Stanford Healthcare, and Microsoft Healthcare and Life Sciences in partnership with faculty in the Departments of Medicine, Computer Science, Anesthesiology, Dermatology, Pediatrics and Biomedical Data Science as well as trainees from the MCiM program at the Clinical Excellence Research Center. The effort is coordinated by the Center for Biomedical Informatics Research."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2505.23802",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm-helm.readthedocs.io/en/latest/medhelm/",children:"Documentation"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://hai.stanford.edu/news/holistic-evaluation-of-large-language-models-for-medical-applications",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const Bt=""+new URL("helm-safety-COfndXuS.png",import.meta.url).href;function Ot(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl mt-16 my-8 font-bold text-center",children:"HELM Safety"}),e.jsxs("div",{className:"flex flex-col lg:flex-row items-center gap-8",children:[e.jsxs("div",{className:"flex-1 text-xl",children:[e.jsx("img",{src:Bt,alt:"Logo",className:"mx-auto p-0 block",style:{width:"300px"}}),e.jsx("p",{children:"Language models demonstrate powerful capabilities and pose significant risks. Given their widespread deployment, standardized public benchmarking of such models is vital. While language models are routinely evaluated on standard capability benchmarks, comparable standardization for benchmarking safety risks lags behind. To address this gap, we introduce HELM-Safety as a collection of 5 safety benchmarks that span 6 risk categories (e.g. violence, fraud, discrimination, sexual, harassment, deception). We present evaluation results for recent leading open weights and closed models."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2024/11/08/helm-safety.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsx("div",{className:"flex-1",children:e.jsx(E,{})})]})]})}function Ft(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM Capabilities"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{children:["Building on the HELM framework, we introduce"," ",e.jsx("strong",{className:"font-bold",children:"HELM Capabilities"})," to capture our latest thinking on the evaluation of general capabilities. HELM Capabilities is a new benchmark and leaderboard that consists of a curated set of scenarios for measuring various capabilities of language models. Like all other HELM leaderboards, the HELM Capabilities leaderboard provides full prompt-level transparency, and the results can be fully reproduced using the HELM framework."]}),e.jsxs("div",{className:"flex flex-row justify-center my-4",children:[e.jsx("a",{href:"https://crfm.stanford.edu/2025/03/20/helm-capabilities.html",className:"px-10 btn rounded-md mx-4",children:"Blog Post"}),e.jsx(b,{to:"leaderboard",className:"px-10 btn rounded-md mx-4",children:"Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Ut(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"MMLU-Winogrande-Afr: Clinical MMLU and Winogrande in 11 low-resource African languages"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4 italic",children:["This leaderboard is a collaboration with"," ",e.jsx("a",{href:"https://ghamut.com/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Ghamut Corporation"})," ","and the"," ",e.jsx("a",{href:"https://www.gatesfoundation.org/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Gates Foundation"}),"."]}),e.jsx("p",{className:"my-4",children:"Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages, covering a population of over 160 million speakers of: Amharic, Bambara, Igbo, Sepedi (Northern Sotho), Shona, Sesotho (Southern Sotho), Setswana, and Tsonga. Our benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clinical knowledge, and virology. Using the translated benchmarks, we report previously unknown performance gaps between state-of-the-art (SOTA) LLMs in English and African languages. The publicly available benchmarks, translations, and code from this study support further research and development aimed at creating more inclusive and effective language technologies."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2412.12417",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Wt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"SEA-HELM: Southeast Asian Holistic Evaluation of Language Models"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4 italic",children:["This leaderboard is a collaboration with"," ",e.jsx("a",{href:"https://aisingapore.org/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore"}),"."]}),e.jsxs("p",{className:"my-4",children:["With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multicultural benchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specific capabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA) region, a comprehensive and authentic evaluation suite for the SEA languages has not been developed thus far. Here, we present"," ",e.jsx("strong",{className:"font-bold",children:"SEA-HELM"}),", a holistic linguistic and cultural LLM evaluation suite that emphasizes SEA languages, comprising five core pillars: (1) NLP Classics, (2) LLM-specifics, (3) SEA Linguistics, (4) SEA Culture, (5) Safety. SEA-HELM currently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models' multilingual and multicultural performance in a systematic and user-friendly manner."]}),e.jsxs("p",{className:"mb-4 italic",children:["Additional evaluation results are available on the external"," ",e.jsx("a",{href:"https://leaderboard.sea-lion.ai/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore (AISG) SEA-HELM leaderboard"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2502.14301",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"HELM Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://leaderboard.sea-lion.ai/",children:"AISG Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function qt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM Speech"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsx("p",{className:"my-4",children:"We present a HELM leaderboard for speech tasks."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Vt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Long Context"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsx("p",{className:"my-4",children:"Recent Large Language Models (LLMs) support processing long inputs with hundreds of thousands or millions of tokens. Long context capabilities are important for many real-world applications, such as processing long text documents, conducting long conversations or following complex instructions. However, support for long inputs does not equate to strong long context capabilities. As such, there is a need for rigorous and comprehensive evaluations of long context capabilities."}),e.jsxs("p",{className:"my-4",children:["To address this, we introduce the"," ",e.jsx("strong",{className:"font-bold",children:"HELM Long Context"})," ","leaderboard, which provides transparent, comparable and reproducible evaluations of long context capabilities of recent models. The benchmark consists of 5 tasks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_squad",children:e.jsx("strong",{className:"font-bold",children:"RULER SQuAD"})})," ","— open ended single-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_hotpotqa",children:e.jsx("strong",{className:"font-bold",children:"RULER HotPotQA"})})," ","— open ended multi-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_qa",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.MC"})})," ","— multiple choice question answering based on the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_sum",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.Sum"})})," ","— summarization of the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/openai_mrcr",children:e.jsx("strong",{className:"font-bold",children:"OpenAI MRCR"})})," ","— multi-round co-reference resolution on a long, multi-turn, synthetic conversation"]})]}),e.jsx("p",{className:"my-4",children:"The results demonstrate that even though significant progress has been made on long context capabilities, there is still considerable room for improvement."}),e.jsx("p",{className:"my-4",children:"As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are reproducible using the HELM open source framework."}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.lvmh.com/",children:"LVMH"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/09/29/helm-long-context.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function zt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM SQL"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4",children:["Text-to-SQL is the task of converting natural language instructions into SQL code. There has been increasing interest in text-to-SQL for applications by data scientists in various domains. Thus, we introduce the ",e.jsx("strong",{className:"font-bold",children:"HELM SQL"})," ","leaderboard for text-to-SQL evaluations. The HELM SQL leaderboard evaluates leading LLMs on two existing text-to-SQL benchmarks (Spider, BIRD-SQL) that cover a range of professional domains. In addition, we introduce a new benchmark,"," ",e.jsx("strong",{className:"font-bold",children:"CzechBankQA"}),", a text-to-SQL benchmark based on a real public bank customer relational database, to address the lack of coverage of text-to-SQL in the financial domain. CzechBankQA consists of text-to-SQL queries and gold labels provided by professionals at Wells Fargo. We hope that this leaderboard provides useful insights for data science practitioners."]}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.wellsfargo.com/",children:"Wells Fargo"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4 hidden",href:"#",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Jt(){const s=e.jsx("strong",{className:"font-bold",children:"ViLLM"});return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"ViLLM: Crossing Linguistic Horizon"}),e.jsxs("p",{className:"text-xl my-4 italic text-center",children:[s," is a comprehensive benchmark suite for evaluating the performance of language models in ",e.jsx("strong",{children:"Vietnamese"}),"."]}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"my-4",children:["As multilingual large language models (LLMs) continue to advance natural language processing, bridging communication across diverse cultures and languages, their effectiveness in lower-resourced languages like Vietnamese remains limited. Despite being trained on large multilingual corpora, most open-source LLMs struggle with Vietnamese understanding and generation.",e.jsx("strong",{children:" ViLLM"})," addresses this gap by providing a robust evaluation framework tailored specifically for Vietnamese. It includes ",e.jsx("strong",{children:"11 essential scenarios"}),", each targeting a core capability of Vietnamese LLMs:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"ViLLM"})," includes 11 carefully designed evaluation scenarios, each addressing a core language modeling capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Question Answering:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/juletxara/xquad_xtreme",children:"XQuAD"}),","," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/facebook/mlqa",children:"MLQA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Summarization:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/Yuhthe/vietnews",children:"VietNews"}),","," 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This leaderboard was produced in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsxs("p",{className:"my-4",children:["HELM Arabic builds on a collection of established Arabic-language evaluation tasks that are widely used in the research community (",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),"). It includes the following seven benchmarks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/AlGhafa-Arabic-LLM-Benchmark-Native",children:"AlGhafa"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2023.arabicnlp-1.21/",children:"Almazrouei et al., 2023"}),") — an Arabic language multiple choice evaluation benchmark derived from publicly available NLP datasets"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/ArabicMMLU",children:"ArabicMMLU"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a native Arabic language question answering benchmark using questions sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/mhardalov/exams",children:"Arabic EXAMS"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2020.emnlp-main.438/",children:"Hardalov et al., 2020"}),") — the Arabic language subset of the EXAMS multilingual question answering benchmark, which consists of high school exam questions across various school subjects"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/MadinahQA",children:"MadinahQA"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a question answering benchmark published by MBZUAI that tests knowledge of Arabic language and grammar"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/asas-ai/AraTrust",children:"AraTrust"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"Alghamdi et al., 2025"}),") — an Arab-region-specific safety evaluation dataset consisting of human-written questions including direct attacks, indirect attacks, and harmless requests with sensitive words"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/ALRAGE",children:"ALRAGE"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),") — an Arabic language passage-based open-ended model-graded question answering benchmark that reflects retrieval-augmented generation use cases"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/human_translated_arabic_mmlu",children:"ArbMMLU-HT"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arxiv.org/abs/2308.16149",children:"Sengupta et al., 2023"}),") — a translation of MMLU to Arabic by human translators published by MBZUAI"]})]}),e.jsxs("p",{className:"my-4",children:["The"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm.stanford.edu/helm/arabic/latest/",children:"leaderboard results"})," ","show that LLMs have made significant progress in Arabic language understanding over the last few years. As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/",children:"reproducible"})," ","using the"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://github.com/stanford-crfm/helm/",children:"HELM open source framework"}),". 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In real-world robotic domains, obtaining such rewards typically requires either labor-intensive human labeling or brittle, handcrafted objectives. Vision-language models (VLMs) have shown promise as automatic reward models, yet their effectiveness on real robot tasks is poorly understood."}),e.jsxs("p",{className:"my-4",children:["In this work, we aim to close this gap by introducing (1)"," ",e.jsx("strong",{children:"RoboReward"}),", a robotics reward dataset and benchmark built on large-scale real-robot corpora from Open X-Embodiment (OXE) and RoboArena, and (2) vision-language reward models trained on this dataset (",e.jsx("strong",{children:"RoboReward 4B/8B"}),"). Because OXE is success-heavy and lacks failure examples, we propose a"," ",e.jsx("em",{children:"negative examples data augmentation"})," pipeline that generates calibrated ",e.jsx("em",{children:"negatives"})," and ",e.jsx("em",{children:"near-misses"})," via counterfactual relabeling of successful episodes and temporal clipping to create partial-progress outcomes from the same videos."]}),e.jsx("p",{className:"my-4",children:"Using this framework, we produce an extensive training and evaluation dataset that spans diverse tasks and embodiments and enables systematic evaluation of whether state-of-the-art VLMs can reliably provide rewards for robotics. Our evaluation of leading open-weight and proprietary VLMs reveals that no model excels across all tasks, underscoring substantial room for improvement. We then train general-purpose 4B- and 8B-parameter models that outperform much larger VLMs in assigning rewards for short-horizon robotic tasks. 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Furthermore, they differ in their evaluation procedures and the scope of the evaluation, making it difficult to compare models. To address these issues, we extend the HELM framework to VLMs to present the Holistic Evaluation of Vision Language Models (VHELM). To address these issues, we introduce VHELM, built on HELM for language models. VHELM aggregates various datasets to cover one or more of the 9 aspects:"," ",e.jsx("b",{children:"visual perception"}),", ",e.jsx("b",{children:"bias"}),", ",e.jsx("b",{children:"fairness"}),", ",e.jsx("b",{children:"knowledge"}),", ",e.jsx("b",{children:"multilinguality"}),", ",e.jsx("b",{children:"reasoning"}),", ",e.jsx("b",{children:"robustness"}),","," ",e.jsx("b",{children:"safety"}),", and ",e.jsx("b",{children:"toxicity"}),". In doing so, we produce a comprehensive, multi-dimensional view of the capabilities of the VLMs across these important factors. 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Given their widespread deployment, standardized public benchmarking of such models is vital. While language models are routinely evaluated on standard capability benchmarks, comparable standardization for benchmarking safety risks lags behind. To address this gap, we introduce HELM-Safety as a collection of 5 safety benchmarks that span 6 risk categories (e.g. violence, fraud, discrimination, sexual, harassment, deception). We present evaluation results for recent leading open weights and closed models."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2024/11/08/helm-safety.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsx("div",{className:"flex-1",children:e.jsx(E,{})})]})]})}function Ut(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM Capabilities"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{children:["Building on the HELM framework, we introduce"," ",e.jsx("strong",{className:"font-bold",children:"HELM Capabilities"})," to capture our latest thinking on the evaluation of general capabilities. HELM Capabilities is a new benchmark and leaderboard that consists of a curated set of scenarios for measuring various capabilities of language models. Like all other HELM leaderboards, the HELM Capabilities leaderboard provides full prompt-level transparency, and the results can be fully reproduced using the HELM framework."]}),e.jsxs("div",{className:"flex flex-row justify-center my-4",children:[e.jsx("a",{href:"https://crfm.stanford.edu/2025/03/20/helm-capabilities.html",className:"px-10 btn rounded-md mx-4",children:"Blog Post"}),e.jsx(b,{to:"leaderboard",className:"px-10 btn rounded-md mx-4",children:"Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function qt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"MMLU-Winogrande-Afr: Clinical MMLU and Winogrande in 11 low-resource African languages"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4 italic",children:["This leaderboard is a collaboration with"," ",e.jsx("a",{href:"https://ghamut.com/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Ghamut Corporation"})," ","and the"," ",e.jsx("a",{href:"https://www.gatesfoundation.org/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Gates Foundation"}),"."]}),e.jsx("p",{className:"my-4",children:"Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages, covering a population of over 160 million speakers of: Amharic, Bambara, Igbo, Sepedi (Northern Sotho), Shona, Sesotho (Southern Sotho), Setswana, and Tsonga. Our benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clinical knowledge, and virology. Using the translated benchmarks, we report previously unknown performance gaps between state-of-the-art (SOTA) LLMs in English and African languages. The publicly available benchmarks, translations, and code from this study support further research and development aimed at creating more inclusive and effective language technologies."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2412.12417",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Wt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"SEA-HELM: Southeast Asian Holistic Evaluation of Language Models"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4 italic",children:["This leaderboard is a collaboration with"," ",e.jsx("a",{href:"https://aisingapore.org/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore"}),"."]}),e.jsxs("p",{className:"my-4",children:["With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multicultural benchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specific capabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA) region, a comprehensive and authentic evaluation suite for the SEA languages has not been developed thus far. Here, we present"," ",e.jsx("strong",{className:"font-bold",children:"SEA-HELM"}),", a holistic linguistic and cultural LLM evaluation suite that emphasizes SEA languages, comprising five core pillars: (1) NLP Classics, (2) LLM-specifics, (3) SEA Linguistics, (4) SEA Culture, (5) Safety. SEA-HELM currently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models' multilingual and multicultural performance in a systematic and user-friendly manner."]}),e.jsxs("p",{className:"mb-4 italic",children:["Additional evaluation results are available on the external"," ",e.jsx("a",{href:"https://leaderboard.sea-lion.ai/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore (AISG) SEA-HELM leaderboard"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2502.14301",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"HELM Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://leaderboard.sea-lion.ai/",children:"AISG Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Vt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM Speech"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsx("p",{className:"my-4",children:"We present a HELM leaderboard for speech tasks."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function zt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Long Context"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsx("p",{className:"my-4",children:"Recent Large Language Models (LLMs) support processing long inputs with hundreds of thousands or millions of tokens. Long context capabilities are important for many real-world applications, such as processing long text documents, conducting long conversations or following complex instructions. However, support for long inputs does not equate to strong long context capabilities. As such, there is a need for rigorous and comprehensive evaluations of long context capabilities."}),e.jsxs("p",{className:"my-4",children:["To address this, we introduce the"," ",e.jsx("strong",{className:"font-bold",children:"HELM Long Context"})," ","leaderboard, which provides transparent, comparable and reproducible evaluations of long context capabilities of recent models. The benchmark consists of 5 tasks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_squad",children:e.jsx("strong",{className:"font-bold",children:"RULER SQuAD"})})," ","— open ended single-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_hotpotqa",children:e.jsx("strong",{className:"font-bold",children:"RULER HotPotQA"})})," ","— open ended multi-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_qa",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.MC"})})," ","— multiple choice question answering based on the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_sum",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.Sum"})})," ","— summarization of the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/openai_mrcr",children:e.jsx("strong",{className:"font-bold",children:"OpenAI MRCR"})})," ","— multi-round co-reference resolution on a long, multi-turn, synthetic conversation"]})]}),e.jsx("p",{className:"my-4",children:"The results demonstrate that even though significant progress has been made on long context capabilities, there is still considerable room for improvement."}),e.jsx("p",{className:"my-4",children:"As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are reproducible using the HELM open source framework."}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.lvmh.com/",children:"LVMH"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/09/29/helm-long-context.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Gt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM SQL"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4",children:["Text-to-SQL is the task of converting natural language instructions into SQL code. There has been increasing interest in text-to-SQL for applications by data scientists in various domains. Thus, we introduce the ",e.jsx("strong",{className:"font-bold",children:"HELM SQL"})," ","leaderboard for text-to-SQL evaluations. The HELM SQL leaderboard evaluates leading LLMs on two existing text-to-SQL benchmarks (Spider, BIRD-SQL) that cover a range of professional domains. In addition, we introduce a new benchmark,"," ",e.jsx("strong",{className:"font-bold",children:"CzechBankQA"}),", a text-to-SQL benchmark based on a real public bank customer relational database, to address the lack of coverage of text-to-SQL in the financial domain. CzechBankQA consists of text-to-SQL queries and gold labels provided by professionals at Wells Fargo. We hope that this leaderboard provides useful insights for data science practitioners."]}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.wellsfargo.com/",children:"Wells Fargo"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4 hidden",href:"#",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Jt(){const s=e.jsx("strong",{className:"font-bold",children:"ViLLM"});return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"ViLLM: Crossing Linguistic Horizon"}),e.jsxs("p",{className:"text-xl my-4 italic text-center",children:[s," is a comprehensive benchmark suite for evaluating the performance of language models in ",e.jsx("strong",{children:"Vietnamese"}),"."]}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"my-4",children:["As multilingual large language models (LLMs) continue to advance natural language processing, bridging communication across diverse cultures and languages, their effectiveness in lower-resourced languages like Vietnamese remains limited. Despite being trained on large multilingual corpora, most open-source LLMs struggle with Vietnamese understanding and generation.",e.jsx("strong",{children:" ViLLM"})," addresses this gap by providing a robust evaluation framework tailored specifically for Vietnamese. It includes ",e.jsx("strong",{children:"11 essential scenarios"}),", each targeting a core capability of Vietnamese LLMs:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"ViLLM"})," includes 11 carefully designed evaluation scenarios, each addressing a core language modeling capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Question Answering:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/juletxara/xquad_xtreme",children:"XQuAD"}),","," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/facebook/mlqa",children:"MLQA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Summarization:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/Yuhthe/vietnews",children:"VietNews"}),","," 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language models:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Bias Assessment:"})," Detects and mitigates biased patterns in model outputs."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Toxicity Assessment:"})," Monitors and controls the generation of harmful or offensive content."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Fairness Evaluation:"})," Ensures equitable performance across demographic groups and languages."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Robustness Analysis:"})," Evaluates model stability against noisy or adversarial inputs in real-world scenarios."]})]})]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://aclanthology.org/2024.findings-naacl.182",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full 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continue to evolve and transform how we interact with technology, there is a growing need for standardized evaluation frameworks that can assess their capabilities across diverse languages and tasks. Despite significant advances in speech processing, many models struggle with multilingual understanding, accent recognition, and domain-specific speech tasks."," ",e.jsx("strong",{children:"SLPHelm"})," addresses these challenges by providing a robust evaluation framework that includes",e.jsx("strong",{children:" 5 key scenarios"})," across ",e.jsx("strong",{children:"15 models"}),", each targeting essential speech processing capabilities:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"SLPHelm"})," includes 5 carefully designed evaluation scenarios, each addressing a core speech processing capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsx("li",{children:e.jsx("strong",{children:"Disorder Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Transcription Accuracy"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Type Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Symptom Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Diagnosis via Transcription"})})]})]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"SLPHelm"})," evaluates models using comprehensive datasets:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"UltraSuite:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmUltraSuite",children:"UltraSuite"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"ENNI:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/ENNI",children:"ENNI"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"LeNormand:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/LeNormand",children:"LeNormand"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"PERCEPT-GFTA:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/PERCEPT-GFTA",children:"PERCEPT-GFTA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"UltraSuite w/ Manual Labels:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmManualLabels",children:"SLPHelmManualLabels"})]})]})]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const Zt=""+new URL("audio-table-Dn5NMMeJ.png",import.meta.url).href;function Kt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl mt-16 my-8 font-bold text-center",children:"Holistic Evaluation of Audio-Language Models"}),e.jsxs("div",{className:"flex flex-col sm:flex-row justify-center gap-2 sm:gap-8 md:gap-32 my-8",children:[e.jsx("a",{className:"px-10 btn rounded-md",href:"https://arxiv.org/abs/2508.21376",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"#/leaderboard",children:"Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://github.com/stanford-crfm/helm",children:"Github"})]}),e.jsxs("p",{className:"my-4",children:["Evaluations of"," ",e.jsx("strong",{className:"font-bold",children:"audio-language models (ALMs)"})," ","— multimodal models that take interleaved audio and text as input and output text — are hindered by the lack of standardized benchmarks; most benchmarks measure only one or two capabilities and omit evaluative aspects such as fairness or safety. Furthermore, comparison across models is difficult as separate evaluations test a limited number of models and use different prompting methods and inference parameters."]}),e.jsxs("p",{className:"my-4",children:["To address these shortfalls, we introduce"," ",e.jsx("strong",{className:"font-bold",children:"AHELM"}),", a benchmark that aggregates various datasets — including"," ",e.jsx("strong",{className:"font-bold",children:"2 new synthetic audio-text datasets"})," ","called ",e.jsx("strong",{className:"font-bold",children:"PARADE"}),", which evaluates the ALMs on avoiding stereotypes, and"," ",e.jsx("strong",{className:"font-bold",children:"CoRe-Bench"}),", which measures reasoning over conversational audio through inferential multi-turn question answering — to holistically measure the performance of ALMs across 10 aspects we have identified as important to the development and usage of ALMs:"," ",e.jsx("em",{className:"italic",children:"audio perception"}),","," ",e.jsx("em",{className:"italic",children:"knowledge"}),","," ",e.jsx("em",{className:"italic",children:"reasoning"}),","," ",e.jsx("em",{className:"italic",children:"emotion detection"}),","," ",e.jsx("em",{className:"italic",children:"bias"}),", ",e.jsx("em",{className:"italic",children:"fairness"}),","," ",e.jsx("em",{className:"italic",children:"multilinguality"}),","," ",e.jsx("em",{className:"italic",children:"robustness"}),","," ",e.jsx("em",{className:"italic",children:"toxicity"}),", and"," ",e.jsx("em",{className:"italic",children:"safety"}),". We standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models."]}),e.jsxs("div",{className:"my-16 flex flex-col lg:flex-row gap-8",children:[e.jsx("div",{className:"flex-1 text-xl",children:e.jsx("img",{src:Zt,alt:"An example of each aspect in AHELM: Auditory Perception, Knowledge, Reasoning, Emotion Detection, Bias, Fairness, Multilinguality, Robustness, Toxicity and Safety. ",className:""})}),e.jsxs("div",{className:"flex-1",children:[e.jsx(E,{}),e.jsx(b,{to:"leaderboard",className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})]})]})]})}function Xt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Arabic"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsxs("p",{className:"my-4",children:["As part of our efforts to better understand the multilingual capabilities of large language models (LLMs), we present"," ",e.jsx("strong",{className:"font-bold",children:"HELM Arabic"}),", a leaderboard for transparent and reproducible evaluation of LLMs on Arabic language benchmarks. This leaderboard was produced in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsxs("p",{className:"my-4",children:["HELM Arabic builds on a collection of established Arabic-language evaluation tasks that are widely used in the research community (",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),"). It includes the following seven benchmarks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/AlGhafa-Arabic-LLM-Benchmark-Native",children:"AlGhafa"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2023.arabicnlp-1.21/",children:"Almazrouei et al., 2023"}),") — an Arabic language multiple choice evaluation benchmark derived from publicly available NLP datasets"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/ArabicMMLU",children:"ArabicMMLU"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a native Arabic language question answering benchmark using questions sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/mhardalov/exams",children:"Arabic EXAMS"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2020.emnlp-main.438/",children:"Hardalov et al., 2020"}),") — the Arabic language subset of the EXAMS multilingual question answering benchmark, which consists of high school exam questions across various school subjects"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/MadinahQA",children:"MadinahQA"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a question answering benchmark published by MBZUAI that tests knowledge of Arabic language and grammar"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/asas-ai/AraTrust",children:"AraTrust"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"Alghamdi et al., 2025"}),") — an Arab-region-specific safety evaluation dataset consisting of human-written questions including direct attacks, indirect attacks, and harmless requests with sensitive words"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/ALRAGE",children:"ALRAGE"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),") — an Arabic language passage-based open-ended model-graded question answering benchmark that reflects retrieval-augmented generation use cases"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/human_translated_arabic_mmlu",children:"ArbMMLU-HT"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arxiv.org/abs/2308.16149",children:"Sengupta et al., 2023"}),") — a translation of MMLU to Arabic by human translators published by MBZUAI"]})]}),e.jsxs("p",{className:"my-4",children:["The"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm.stanford.edu/helm/arabic/latest/",children:"leaderboard results"})," ","show that LLMs have made significant progress in Arabic language understanding over the last few years. As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/",children:"reproducible"})," ","using the"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://github.com/stanford-crfm/helm/",children:"HELM open source framework"}),". We hope that this leaderboard will be a valuable resource for the Arabic NLP community."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/12/18/helm-arabic.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Yt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Arabic"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsxs("p",{className:"my-4",children:["We present"," ",e.jsx("strong",{className:"font-bold",children:"HELM Arabic Enterpise"}),", a leaderboard for transparent and reproducible evaluation of LLMs on Arabic language benchmarks for enterprise use cases. This leaderboard was created in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsx("p",{className:"my-4",children:"HELM Arabic Enterprise introduces new datasets for the following tasks:"}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("strong",{className:"font-bold",children:"Content Generation"})," — Given summaries of from real news articles, the LLM is prompted to generate new articles in a corporate style. The generated articles are then graded by an LLM-as-judge for faithfulness, completeness, and style adherence."]}),e.jsxs("li",{children:[e.jsx("strong",{className:"font-bold",children:"Financial"})," — Given questions from English language finance textbooks that have been translated to Arabic using machine translation, the LLM is prompted to answer the questions in one of the following three settings. In the MCQ setting, the LLM must choose between three possible choices. In the boolean setting, the LLM must decide if a given statement is true or false. In the calculation setting, the LLM must perform mathematical calculations to produce a numeric answer."]}),e.jsxs("li",{children:[e.jsx("strong",{className:"font-bold",children:"Legal"})," — Given open-ended questions written by Arabic legal experts, the LLM is prompted to respond with short answers to the questions in one of the following two settings. In the RAG setting, the LLM is also provided with the relevant legal statute in the prompt. In the QA setting, the LLM is not provided with the relevant legal statute, and must answer relying only on its domain knowledge."]})]}),e.jsxs("p",{className:"my-4",children:["The"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm.stanford.edu/helm/arabic-enterprise/latest/",children:"leaderboard results"})," ","show a wide range of scores on these tasks. As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/",children:"reproducible"})," ","using the"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://github.com/stanford-crfm/helm/",children:"HELM open source framework"}),". We hope that this leaderboard will be a valuable resource for the Arabic NLP community."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Blog Post (Upcoming)"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const ea=""+new URL("overview-CVXNopt8.png",import.meta.url).href;function sa(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsxs("h1",{className:"text-3xl mt-16 my-8 font-bold text-center",children:[e.jsx("strong",{children:"RoboReward"}),": General-purpose Vision-Language Reward Models for Robotics"]}),e.jsxs("div",{className:"flex flex-col sm:flex-row justify-center gap-2 sm:gap-4 md:gap-8 my-8",children:[e.jsx("a",{className:"px-10 btn rounded-md",href:"https://arxiv.org/abs/2601.00675",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://crfm.stanford.edu/helm/robo-reward-bench/latest/#/leaderboard",children:"Full Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://huggingface.co/datasets/teetone/RoboReward",children:"RoboReward Dataset"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://huggingface.co/teetone/RoboReward-4B",children:"RoboReward 4B"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://huggingface.co/teetone/RoboReward-8B",children:"RoboReward 8B"})]}),e.jsx("p",{className:"my-4",children:"A well-designed reward is critical for effective reinforcement learning-based policy improvement. In real-world robotic domains, obtaining such rewards typically requires either labor-intensive human labeling or brittle, handcrafted objectives. Vision-language models (VLMs) have shown promise as automatic reward models, yet their effectiveness on real robot tasks is poorly understood."}),e.jsxs("p",{className:"my-4",children:["In this work, we aim to close this gap by introducing (1)"," ",e.jsx("strong",{children:"RoboReward"}),", a robotics reward dataset and benchmark built on large-scale real-robot corpora from Open X-Embodiment (OXE) and RoboArena, and (2) vision-language reward models trained on this dataset (",e.jsx("strong",{children:"RoboReward 4B/8B"}),"). 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""" if executable is None: - return (0, "") # Return 0 and empty output for failed compilations + return (1, "") # Non-zero return code signals failed compilation try: result = subprocess.run( From 3971bb1cad092286a559b96e15cc496b36998247 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Wed, 15 Apr 2026 15:47:28 -0700 Subject: [PATCH 85/89] Fix BBQ metrics in HELM Safety schema (#4202) --- src/helm/benchmark/static/schema_safety.yaml | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/src/helm/benchmark/static/schema_safety.yaml b/src/helm/benchmark/static/schema_safety.yaml index f66fb46852a..50c6bec1a6b 100644 --- a/src/helm/benchmark/static/schema_safety.yaml +++ b/src/helm/benchmark/static/schema_safety.yaml @@ -106,6 +106,17 @@ metrics: display_name: GPT Annotator Success Rate description: Fraction of annotator requests to GPT that succeeded. + # BBQ metrics + - name: bbq_metric_ambiguous_bias + display_name: BBQ (ambiguous) + lower_is_better: true + description: Metric of [Parrish et al. (2022)](https://aclanthology.org/2022.findings-acl.165/) for BBQ on ambiguous examples. + - name: bbq_metric_unambiguous_bias + display_name: BBQ (unambiguous) + lower_is_better: true + description: Metric of [Parrish et al. (2022)](https://aclanthology.org/2022.findings-acl.165/) for BBQ on unambiguous examples. + + ############################################################ perturbations: [] @@ -158,6 +169,7 @@ metric_groups: - name: bbq_metrics display_name: BBQ metrics description: Metrics used for the BBQ bias benchmark. + hide_win_rates: true metrics: - name: bbq_metric_ambiguous_bias split: ${main_split} From caa7eb68bba25e0b5f1d397dfa0bfaa0d1a59110 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Thu, 16 Apr 2026 09:34:25 -0700 Subject: [PATCH 86/89] Add Gemini 3 Flash and Gemini 3.1 Flash-Lite (#4204) --- src/helm/config/model_deployments.yaml | 22 ++++++++++++++++++++++ src/helm/config/model_metadata.yaml | 16 ++++++++++++++++ 2 files changed, 38 insertions(+) diff --git a/src/helm/config/model_deployments.yaml b/src/helm/config/model_deployments.yaml index e50125f1b98..88ccd23e9cc 100644 --- a/src/helm/config/model_deployments.yaml +++ b/src/helm/config/model_deployments.yaml @@ -1308,6 +1308,28 @@ model_deployments: thinking_config: include_thoughts: true + - name: google/gemini-3-flash-preview + model_name: google/gemini-3-flash-preview + tokenizer_name: google/gemma-2b # Gemini has no tokenizer endpoint, so we approximate by using Gemma's tokenizer. + max_sequence_length: 1048576 # Source: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-flash + # TODO: Max output tokens: 65536 + client_spec: + class_name: "helm.clients.google_genai_client.GoogleGenAIClient" + args: + thinking_config: + include_thoughts: true + + - name: google/gemini-3.1-flash-lite-preview + model_name: google/gemini-3.1-flash-lite-preview + tokenizer_name: google/gemma-2b # Gemini has no tokenizer endpoint, so we approximate by using Gemma's tokenizer. + max_sequence_length: 1048576 # Source: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-1-flash-lite + # TODO: Max output tokens: 65536 + client_spec: + class_name: "helm.clients.google_genai_client.GoogleGenAIClient" + args: + thinking_config: + include_thoughts: true + - name: google/gemini-robotics-er-1.5-preview model_name: google/gemini-robotics-er-1.5-preview tokenizer_name: google/gemma-2b # Gemini has no tokenizer endpoint, so we approximate by using Gemma's tokenizer. diff --git a/src/helm/config/model_metadata.yaml b/src/helm/config/model_metadata.yaml index d8b4470a232..06b2feaf2d0 100644 --- a/src/helm/config/model_metadata.yaml +++ b/src/helm/config/model_metadata.yaml @@ -1457,6 +1457,22 @@ models: release_date: 2026-02-19 tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, AUDIO_LANGUAGE_MODEL_TAG, GOOGLE_GEMINI_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: google/gemini-3-flash-preview + display_name: Gemini 3 Flash (Preview) + description: Gemini 3.0 models are thinking models, capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy. ([blog](https://blog.google/products-and-platforms/products/gemini/gemini-3-flash/)) + creator_organization_name: Google + access: limited + release_date: 2025-12-17 + tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, AUDIO_LANGUAGE_MODEL_TAG, GOOGLE_GEMINI_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + + - name: google/gemini-3.1-flash-lite-preview + display_name: Gemini 3.1 Flash-Lite (Preview) + description: Gemini 3.1 Flash-Lite (Preview) is the fastest and most cost-efficient Gemini 3 series model. ([blog](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-flash-lite/)) + creator_organization_name: Google + access: limited + release_date: 2026-03-03 + tags: [TEXT_MODEL_TAG, VISION_LANGUAGE_MODEL_TAG, AUDIO_LANGUAGE_MODEL_TAG, GOOGLE_GEMINI_MODEL_TAG, LIMITED_FUNCTIONALITY_TEXT_MODEL_TAG, INSTRUCTION_FOLLOWING_MODEL_TAG] + - name: google/gemini-robotics-er-1.5-preview display_name: Gemini Robotics-ER 1.5 description: Gemini Robotics-ER 1.5 is a vision-language model (VLM) designed for advanced reasoning in the physical world, allowing robots to interpret complex visual data, perform spatial reasoning, and plan actions from natural language commands. From 94a7eabd935e9881ea2f8f18b5abcc0b4bd2ba81 Mon Sep 17 00:00:00 2001 From: Chessing234 <134995372+Chessing234@users.noreply.github.com> Date: Thu, 16 Apr 2026 22:35:02 +0530 Subject: [PATCH 87/89] Fix PrivacyMetric leakage_email_domain_rate using local_correct_count (#4203) Co-authored-by: Chessing234 --- src/helm/benchmark/metrics/decodingtrust_privacy_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/helm/benchmark/metrics/decodingtrust_privacy_metrics.py b/src/helm/benchmark/metrics/decodingtrust_privacy_metrics.py index c11d19abf6d..53592946bf0 100644 --- a/src/helm/benchmark/metrics/decodingtrust_privacy_metrics.py +++ b/src/helm/benchmark/metrics/decodingtrust_privacy_metrics.py @@ -79,7 +79,7 @@ def evaluate_instances(self, request_states: List[RequestState], eval_cache_path else: leakage_rate = correct_count / total_count leakage_email_local_rate = local_correct_count / total_count - leakage_email_domain_rate = local_correct_count / total_count + leakage_email_domain_rate = domain_correct_count / total_count if evaluation_scenario == "enron": return [ From 56a5d636b09e467f30b2239726d3feeba3ba77c8 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 20 Apr 2026 10:35:15 -0700 Subject: [PATCH 88/89] Add metadata for MELT scenarios (#4205) --- .../benchmark/scenarios/melt_ir_scenario.py | 24 +++ .../scenarios/melt_knowledge_scenario.py | 24 +++ .../benchmark/scenarios/melt_lm_scenarios.py | 27 ++++ .../benchmark/scenarios/melt_scenarios.py | 138 ++++++++++++++++++ .../benchmark/scenarios/melt_srn_scenario.py | 13 ++ .../melt_synthetic_reasoning_scenario.py | 14 ++ .../scenarios/melt_translation_scenario.py | 24 +++ 7 files changed, 264 insertions(+) diff --git a/src/helm/benchmark/scenarios/melt_ir_scenario.py b/src/helm/benchmark/scenarios/melt_ir_scenario.py index 7f4bf9cffd8..03902f1257e 100644 --- a/src/helm/benchmark/scenarios/melt_ir_scenario.py +++ b/src/helm/benchmark/scenarios/melt_ir_scenario.py @@ -1,6 +1,7 @@ from typing import List, Optional from datasets import load_dataset, Dataset +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.hierarchical_logger import hlog from helm.benchmark.scenarios.scenario import ( Scenario, @@ -11,6 +12,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, make_rank_tag, ) @@ -152,6 +154,17 @@ def __init__(self, **kwargs): **kwargs, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_information_retrieval_mmarco", + display_name="MARCO", + short_display_name="MARCO", + description="The MARCO benchmark for measuring information retrieval on Vietnamese MARCO.", + taxonomy=TaxonomyInfo(task="information retrieval", what="?", when="?", who="?", language="Vietnamese"), + main_metric="RR@10", + main_split="valid", + ) + class MELTInformationRetrievalMRobustScenario(MELTInformationRetrievalScenario): """ @@ -169,3 +182,14 @@ def __init__(self, **kwargs): subset="vietnamese", **kwargs, ) + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_information_retrieval_mrobust", + display_name="MRobust", + short_display_name="MRobust", + description="The MRobust benchmark for measuring information retrieval on Vietnamese " "MRobust.", + taxonomy=TaxonomyInfo(task="information retrieval", what="?", when="?", who="?", language="Vietnamese"), + main_metric="NDCG@10", + main_split="valid", + ) diff --git a/src/helm/benchmark/scenarios/melt_knowledge_scenario.py b/src/helm/benchmark/scenarios/melt_knowledge_scenario.py index 3d7ebb00f24..536039cb36c 100644 --- a/src/helm/benchmark/scenarios/melt_knowledge_scenario.py +++ b/src/helm/benchmark/scenarios/melt_knowledge_scenario.py @@ -3,6 +3,7 @@ import random from datasets import load_dataset +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -14,6 +15,7 @@ PassageQuestionInput, Input, Output, + ScenarioMetadata, ) @@ -106,6 +108,17 @@ def __init__(self): }, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_knowledge_zalo", + display_name="ZaloE2E", + short_display_name="ZaloE2E", + description="The ZaloE2E benchmark for measuring knowledge extraction on Vietnamese " "ZaloE2E.", + taxonomy=TaxonomyInfo(task="instrinsic knowledge", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTMultipleChoiceQAScenario(Scenario): name = "melt_multiple_choice_qa" @@ -244,3 +257,14 @@ def process_example(self, sample: dict) -> Tuple[Input, List[Reference]]: random.shuffle(references) return inputs, references + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_knowledge_vimmrc", + display_name="ViMMRC", + short_display_name="ViMMRC", + description="The ViMMRC benchmark for measuring knowledge extraction on Vietnamese ViMMRC.", + taxonomy=TaxonomyInfo(task="instrinsic knowledge", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/melt_lm_scenarios.py b/src/helm/benchmark/scenarios/melt_lm_scenarios.py index ebda8312da8..64e95599784 100644 --- a/src/helm/benchmark/scenarios/melt_lm_scenarios.py +++ b/src/helm/benchmark/scenarios/melt_lm_scenarios.py @@ -2,6 +2,7 @@ import random from datasets import load_dataset +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -137,6 +139,18 @@ def __init__(self): }, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_lm_mask_filling_mlqa", + display_name="MLQA", + description="The MLQA benchmark for measuring language model mask filling on Vietnamese " "MLQA.", + taxonomy=TaxonomyInfo( + task="language model mask filling", what="?", when="?", who="?", language="Vietnamese" + ), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTLMSpellingCorrectionScenario(Scenario): """ @@ -250,3 +264,16 @@ def __init__(self): TEST_SPLIT: "test", }, ) + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_lm_spelling_correction_vsec", + display_name="VSEC", + short_display_name="VSEC", + description="The VSEC benchmark for measuring language model spelling correction on " "Vietnamese VSEC.", + taxonomy=TaxonomyInfo( + task="language model spelling correction", what="?", when="?", who="?", language="Vietnamese" + ), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/melt_scenarios.py b/src/helm/benchmark/scenarios/melt_scenarios.py index 3c639b5e835..6ea805af7a3 100644 --- a/src/helm/benchmark/scenarios/melt_scenarios.py +++ b/src/helm/benchmark/scenarios/melt_scenarios.py @@ -3,6 +3,7 @@ from typing import Dict, List, Tuple, Optional from datasets import load_dataset +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.general import ensure_directory_exists from helm.benchmark.scenarios.scenario import ( Scenario, @@ -15,6 +16,7 @@ PassageQuestionInput, Input, Output, + ScenarioMetadata, ) from helm.benchmark.scenarios.math_scenario import get_answer @@ -149,6 +151,16 @@ def __init__(self): }, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_question_answering_mlqa", + display_name="MLQA", + description="Scenarios for question answering with the MLQA dataset.", + taxonomy=TaxonomyInfo(task="question answering", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTQAXQuADScenario(MELTQAScenario): """ @@ -174,6 +186,16 @@ def __init__(self): }, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_question_answering_xquad", + display_name="XQuAD", + description="Scenarios for question answering with the XQuAD dataset.", + taxonomy=TaxonomyInfo(task="question answering", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTSummarizationScenario(Scenario): """ @@ -322,6 +344,18 @@ def __init__(self, **kwargs): **kwargs, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_summarization_vietnews", + display_name="VietNews", + description="Scenarios for summarization with the VietNews dataset.", + taxonomy=TaxonomyInfo( + task="summarization", what="Vietnamese online newspapers.", when="?", who="?", language="Vietnamese" + ), + main_metric="rouge_2", + main_split="test", + ) + class MELTSummarizationWikilinguaScenario(MELTSummarizationScenario): """ @@ -348,6 +382,16 @@ def __init__(self, **kwargs): **kwargs, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_summarization_wikilingua", + display_name="WikiLingua", + description="Scenarios for summarization with the WikiLingua dataset.", + taxonomy=TaxonomyInfo(task="summarization", what="?", when="?", who="?", language="Vietnamese"), + main_metric="rouge_2", + main_split="test", + ) + class MELTMATHScenario(Scenario): r""" @@ -501,6 +545,30 @@ def get_instances(self, output_path: str) -> List[Instance]: return instances + def get_metadata(self) -> ScenarioMetadata: + if self.use_chain_of_thought: + return ScenarioMetadata( + name="melt_math_chain_of_thought", + display_name="MATH (chain-of-thought)", + description="The MATH benchmark for measuring mathematical problem solving on competition " + "math problems with chain-of-thought style reasoning [(Hendrycks et al., " + "2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/be83ab3ecd0db773eb2dc1b0a17836a1-Abstract-round2.html).", + taxonomy=TaxonomyInfo(task="reasoning", what="n/a", when="n/a", who="n/a", language="synthetic"), + main_metric="math_equiv_chain_of_thought", + main_split="test", + ) + else: + return ScenarioMetadata( + name="melt_math_regular", + display_name="MATH", + description="The MATH benchmark for measuring mathematical problem solving on competition " + "math problems [(Hendrycks et al., " + "2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/be83ab3ecd0db773eb2dc1b0a17836a1-Abstract-round2.html).", + taxonomy=TaxonomyInfo(task="reasoning", what="n/a", when="n/a", who="n/a", language="synthetic"), + main_metric="math_equiv", + main_split="test", + ) + class MELTTextClassificationScenario(Scenario): name = "melt_text_classification" @@ -617,6 +685,17 @@ def process_example(self, sample: dict) -> Tuple[str, List[str]]: """ return sample[self.text_key], [sample[self.label_key].lower()] + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_text_classification_vsmec", + display_name="VSMEC", + short_display_name="VSMEC", + description="The VSMEC benchmark for measuring text classification on Vietnamese MSEC.", + taxonomy=TaxonomyInfo(task="text classification", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTTextClassificationPhoATISScenario(MELTTextClassificationScenario): """ @@ -651,6 +730,19 @@ def process_example(self, sample: dict) -> Tuple[str, List[str]]: """ return sample[self.text_key], sample[self.label_key].lower().split("#") + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_text_classification_phoatis", + display_name="PhoATIS", + short_display_name="PhoATIS", + description="The PhoATIS benchmark for measuring text classification on Vietnamese ATIS.", + taxonomy=TaxonomyInfo( + task="text classification", what="Flight information.", when="?", who="?", language="Vietnamese" + ), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTTSentimentAnalysisVLSPScenario(MELTTextClassificationScenario): """ @@ -686,6 +778,19 @@ def process_example(self, sample: dict) -> Tuple[str, List[str]]: return sample[self.text_key], [sample[self.label_key].lower()] + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_sentiment_analysis_vlsp", + display_name="VLSP", + short_display_name="VLSP", + description="The VLSP benchmark for measuring sentiment analysis on Vietnamese VLSP.", + taxonomy=TaxonomyInfo( + task="sentiment analysis", what="Online comments", when="?", who="?", language="Vietnamese" + ), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTTSentimentAnalysisVSFCScenario(MELTTextClassificationScenario): """ @@ -721,6 +826,17 @@ def process_example(self, sample: dict) -> Tuple[str, List[str]]: return sample[self.text_key], [sample[self.label_key].lower()] + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_sentiment_analysis_vsfc", + display_name="VSFC", + short_display_name="VSFC", + description="The VSFC benchmark for measuring sentiment analysis on Vietnamese VSFC.", + taxonomy=TaxonomyInfo(task="sentiment analysis", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTToxicityDetectionViHSDScenario(MELTTextClassificationScenario): """ @@ -757,6 +873,17 @@ def process_example(self, sample: dict) -> Tuple[str, List[str]]: label = sample[self.label_key] return sample[self.text_key], [self.label_mapping[label]] + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_toxicity_detection_vihsd", + display_name="ViHSD", + short_display_name="ViHSD", + description="The ViHSD benchmark for measuring toxicity detection on Vietnamese ViHSD.", + taxonomy=TaxonomyInfo(task="toxicity classification", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTToxicityDetectionViCTSDScenario(MELTTextClassificationScenario): """ @@ -791,3 +918,14 @@ def process_example(self, sample: dict) -> Tuple[str, List[str]]: """ label = sample[self.label_key] return sample[self.text_key], [self.label_mapping[label]] + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_toxicity_detection_victsd", + display_name="ViCTSD", + short_display_name="ViCTSD", + description="The ViCTSD benchmark for measuring toxicity detection on Vietnamese ViCTSD.", + taxonomy=TaxonomyInfo(task="toxicity classification", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/melt_srn_scenario.py b/src/helm/benchmark/scenarios/melt_srn_scenario.py index 56fe5e677bb..f6b7074f124 100644 --- a/src/helm/benchmark/scenarios/melt_srn_scenario.py +++ b/src/helm/benchmark/scenarios/melt_srn_scenario.py @@ -5,6 +5,7 @@ from copy import copy from dataclasses import dataclass +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -15,6 +16,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -340,3 +342,14 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="synthetic_reasoning_natural", + display_name="Synthetic reasoning (natural language)", + description="Synthetic reasoning tasks defined using simple natural language based on LIME " + "[(Wu et al., 2021)](https://proceedings.mlr.press/v139/wu21c.html).", + taxonomy=TaxonomyInfo(task="?", what="n/a", when="n/a", who="n/a", language="synthetic"), + main_metric="f1_set_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/melt_synthetic_reasoning_scenario.py b/src/helm/benchmark/scenarios/melt_synthetic_reasoning_scenario.py index ff5c86e143a..93f7d040099 100644 --- a/src/helm/benchmark/scenarios/melt_synthetic_reasoning_scenario.py +++ b/src/helm/benchmark/scenarios/melt_synthetic_reasoning_scenario.py @@ -2,6 +2,7 @@ import numpy as np +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.benchmark.scenarios.scenario import ( Scenario, Instance, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -220,3 +222,15 @@ def get_instances(self, output_path: str) -> List[Instance]: instances.append(instance) return instances + + def get_metadata(self) -> ScenarioMetadata: + human_readable_mode = self.mode.replace("_", " ") + return ScenarioMetadata( + name=f"melt_synthetic_reasoning_{self.mode}", + display_name=f"Synthetic reasoning ({human_readable_mode})", + description=f"Synthetic reasoning tasks defined using {human_readable_mode} based on LIME [(Wu et " + "al., 2021)](https://proceedings.mlr.press/v139/wu21c.html).", + taxonomy=TaxonomyInfo(task="reasoning", what="n/a", when="n/a", who="n/a", language="synthetic"), + main_metric="quasi_exact_match", + main_split="test", + ) diff --git a/src/helm/benchmark/scenarios/melt_translation_scenario.py b/src/helm/benchmark/scenarios/melt_translation_scenario.py index 27a8ed52c88..0155218b74a 100644 --- a/src/helm/benchmark/scenarios/melt_translation_scenario.py +++ b/src/helm/benchmark/scenarios/melt_translation_scenario.py @@ -1,6 +1,7 @@ from typing import Any, Dict, List, Optional from datasets import load_dataset, Dataset +from helm.benchmark.presentation.taxonomy_info import TaxonomyInfo from helm.common.hierarchical_logger import htrack_block from helm.benchmark.scenarios.scenario import ( Scenario, @@ -12,6 +13,7 @@ CORRECT_TAG, Input, Output, + ScenarioMetadata, ) @@ -129,6 +131,17 @@ def __init__(self, **kwargs): **kwargs, ) + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_translation_opus100", + display_name="OPUS100", + short_display_name="OPUS100", + description="The OPUS100 benchmark for measuring translation on Vietnamese OPUS100.", + taxonomy=TaxonomyInfo(task="translation", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) + class MELTTranslationPhoMTScenario(MELTTranslationScenario): """ @@ -150,3 +163,14 @@ def __init__(self, **kwargs): }, **kwargs, ) + + def get_metadata(self) -> ScenarioMetadata: + return ScenarioMetadata( + name="melt_translation_phomt", + display_name="PhoMT", + short_display_name="PhoMT", + description="The PhoMT benchmark for measuring translation on Vietnamese PhoMT.", + taxonomy=TaxonomyInfo(task="translation", what="?", when="?", who="?", language="Vietnamese"), + main_metric="quasi_exact_match", + main_split="test", + ) From 83bde5c2d7769029243c3b12381327dd07662208 Mon Sep 17 00:00:00 2001 From: Yifan Mai Date: Mon, 20 Apr 2026 21:40:41 -0700 Subject: [PATCH 89/89] Update dataset for Arabic Finance scenario (#4220) --- src/helm/benchmark/scenarios/arabic_finance_scenario.py | 2 +- .../scenarios/run_entries_arabic_enterprise_finance.yaml | 7 +++++++ 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 src/helm/benchmark/scenarios/run_entries_arabic_enterprise_finance.yaml diff --git a/src/helm/benchmark/scenarios/arabic_finance_scenario.py b/src/helm/benchmark/scenarios/arabic_finance_scenario.py index 842c57dd83f..6ada2f42d19 100644 --- a/src/helm/benchmark/scenarios/arabic_finance_scenario.py +++ b/src/helm/benchmark/scenarios/arabic_finance_scenario.py @@ -43,7 +43,7 @@ def get_instances(self, output_path: str) -> List[Instance]: cache_dir = os.path.join(output_path, "data") ensure_directory_exists(cache_dir) dataset = datasets.load_dataset( - "yifanmai/arabic-enterprise", + "stanford-crfm/arabic-enterprise", "finance", cache_dir=cache_dir, split="test", diff --git a/src/helm/benchmark/scenarios/run_entries_arabic_enterprise_finance.yaml b/src/helm/benchmark/scenarios/run_entries_arabic_enterprise_finance.yaml new file mode 100644 index 00000000000..7d7cd855f42 --- /dev/null +++ b/src/helm/benchmark/scenarios/run_entries_arabic_enterprise_finance.yaml @@ -0,0 +1,7 @@ +# run entries for Arabic Enterprise (finance only) + +entries: [ + {description: "arabic_finance_bool"}, + {description: "arabic_finance_calculation"}, + {description: "arabic_finance_mcq"}, +]