This repository collects papers related to Causality (CI) and large language models (LLMs).
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[arxiv] A Survey on Hallucination in Large Language Models
2023.09 -
[arxiv] Explainability for Large Language Models: A Survey.
2023.09
TO DO: Add reference you refer to explain causality here:
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[arxiv] Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey.
2024.03 -
[arxiv] Emerging Synergies in Causality and Deep Generative Models: A Survey.
2023.09 -
[arxiv] Causal Reasoning and Large Language Models: Opening a New Frontier for Causality.
2023.04 -
[arxiv] Understanding Causality with Large Language Models: Feasibility and Opportunities.
2023.04
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[arxiv] End-To-End Causal Effect Estimation from Unstructured Natural Language Data.
2024.7 -
[arxiv] Towards Causal Foundation Model: on Duality between Causal Inference and Attention.
2024.6 -
[arxiv] Causal Inference Using LLM-Guided Discovery.
2023.10 -
[IEEE] Does Metacognitive Prompting Improve Causal Inference in Large Language Models?.
2024 -
[arxiv] DISCO: Distilling Counterfactuals with Large Language Models
2023.06
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[ACM] Causal Dataset Discovery with Large Language Models.
2024.06 -
[arxiv] ALCM: Autonomous LLM-Augmented Causal Discovery Framework
2024.05 -
[arxiv] Efficient Causal Graph Discovery Using Large Language Models
2024.02 -
[arxiv] Discovery of the Hidden World with Large Language Models
2024.02 -
[arxiv] Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
2024.02 -
[arxiv] Causal Discovery with Language Models as Imperfect Experts
2023.06 -
[arxiv] From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data
2023.06 -
[arxiv] Can large language models build causal graphs?
2023.04 -
[arxiv] Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis
2023.02 -
[arxiv] Large Language Models for Biomedical Causal Graph Construction?
2023.01 -
[arxiv] Can Foundation Models Talk Causality?
2022.06 -
[arxiv] Causal BERT : Language models for causality detection between events expressed in text?
2021.01
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[arxiv] Causal Structure Learning Supervised by Large Language Model
2023.11 -
[arxiv] Mitigating Prior Errors in Causal Structure Learning: Towards LLM driven Prior Knowledge
2023.6 -
[arxiv] The impact of prior knowledge on causal structure learning
2023.3 -
[arxiv] LMPriors: Pre-Trained Language Models as Task-Specific Priors
2022.10
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[arxiv] Is Knowledge All Large Language Models Needed for Causal Reasoning?
2024.06 -
[arxiv] CELLO: Causal Evaluation of Large Vision-Language Models
2024.06 -
[arxiv] CausalBench: A Comprehensive Benchmark for Causal Learning Capability of Large Language Models?
2024.04 -
[arxiv] Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation.
2023.10 -
[arxiv] Can Large Language Models Infer Causation from Correlation?
2023.06 -
[arxiv] CLadder: Assessing Causal Reasoning in Language Models
2023.06 -
[arxiv] The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code
2023.05 -
[arxiv] Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
2023.05 -
[arxiv] Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
2023.05 -
[arxiv] Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT
2023.3 -
[arxiv] Can Foundation Models Talk Causality?
2022.12 -
[arxiv] CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models
2022.10
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[arxiv] DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification
2023.07 -
[arxiv] Causality-aware Concept Extraction based on Knowledge-guided Prompting
2023.05 -
[ACL] Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning
2023.05 -
[arxiv] Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals
2023.10 -
[AAAI] Debiasing NLU Models via Causal Intervention and Counterfactual Reasoning Authors
2022.06