Improving Causal Reasoning in Large Language Models: A Survey

• URL: http://arxiv.org/abs/2410.16676v3
• Date: Wed, 06 Nov 2024 15:49:30 GMT
• Title: Improving Causal Reasoning in Large Language Models: A Survey
• Authors: Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Qingzhen Liu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan,
• Abstract summary: Causal reasoning is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. Large language models (LLMs) can generate rationales for their outputs, but their ability to reliably perform causal reasoning remains uncertain.
• Score: 16.55801836321059
• License:
• Abstract: Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While large language models (LLMs) can generate rationales for their outputs, their ability to reliably perform causal reasoning remains uncertain, often falling short in tasks requiring a deep understanding of causality. In this survey, we provide a comprehensive review of research aimed at enhancing LLMs for causal reasoning. We categorize existing methods based on the role of LLMs: either as reasoning engines or as helpers providing knowledge or data to traditional CR methods, followed by a detailed discussion of the methodologies in each category. We then evaluate the performance of LLMs on various causal reasoning tasks, providing key findings and in-depth analysis. Finally, we provide insights from current studies and highlight promising directions for future research. We aim for this work to serve as a comprehensive resource, fostering further advancements in causal reasoning with LLMs. Resources are available at https://github.com/chendl02/Awesome-LLM-causal-reasoning.

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