Multi-Agent Causal Discovery Using Large Language Models

• URL: http://arxiv.org/abs/2407.15073v2
• Date: Thu, 10 Oct 2024 02:48:42 GMT
• Title: Multi-Agent Causal Discovery Using Large Language Models
• Authors: Hao Duong Le, Xin Xia, Zhang Chen,
• Abstract summary: Large Language Models (LLMs) have demonstrated significant potential in causal discovery tasks. This paper introduces a general framework to investigate this potential. Our proposed framework shows promising results by effectively utilizing LLMs expert knowledge, reasoning capabilities, multi-agent cooperation, and statistical causal methods.
• Score: 10.020595983728482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated significant potential in causal discovery tasks by utilizing their vast expert knowledge from extensive text corpora. However, the multi-agent capabilities of LLMs in causal discovery remain underexplored. This paper introduces a general framework to investigate this potential. The first is the Meta Agents Model, which relies exclusively on reasoning and discussions among LLM agents to conduct causal discovery. The second is the Coding Agents Model, which leverages the agents' ability to plan, write, and execute code, utilizing advanced statistical libraries for causal discovery. The third is the Hybrid Model, which integrates both the Meta Agents Model and CodingAgents Model approaches, combining the statistical analysis and reasoning skills of multiple agents. Our proposed framework shows promising results by effectively utilizing LLMs expert knowledge, reasoning capabilities, multi-agent cooperation, and statistical causal methods. By exploring the multi-agent potential of LLMs, we aim to establish a foundation for further research in utilizing LLMs multi-agent for solving causal-related problems.

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