用多个大模型协作提升基于评分的因果发现效果
Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery
- 设计多大模型协同框架，利用多个LLM增强因果推断
- 通过融合多个LLM的先验知识，提高因果图重建准确率
- 适合需要高效、低成本获取领域知识的因果研究者
随着现代系统与算法发展中对变量间因果关系理解的重要性日益凸显，从观测数据中学习因果关系已成为优于随机对照试验的优选且高效方法。然而，仅靠观测数据往往不足以重构真实的因果图。因此，许多研究尝试引入某种先验知识以改进因果发现过程。在此背景下，大语言模型（LLMs）的强大能力成为获取昂贵专家知识的有前景替代方案。本文进一步探索利用LLMs增强因果发现方法的潜力，尤其聚焦于基于评分的方法，提出一个通用框架，利用单个甚至多个LLM的能力来辅助发现过程。
原文摘要 · Abstract (English)
As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process.
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