Understanding Causality with Large Language Models: Feasibility and Opportunities

• URL: http://arxiv.org/abs/2304.05524v1
• Date: Tue, 11 Apr 2023 22:30:03 GMT
• Title: Understanding Causality with Large Language Models: Feasibility and Opportunities
• Authors: Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan
• Abstract summary: We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses. We discuss possible future directions and opportunities, such as enabling explicit and implicit causal modules.
• Score: 23.68197884888299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that current LLMs can answer causal questions with existing causal knowledge as combined domain experts. However, they are not yet able to provide satisfactory answers for discovering new knowledge or for high-stakes decision-making tasks with high precision. We discuss possible future directions and opportunities, such as enabling explicit and implicit causal modules as well as deep causal-aware LLMs. These will not only enable LLMs to answer many different types of causal questions for greater impact but also enable LLMs to be more trustworthy and efficient in general.

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