用知识图谱的因果路径增强大模型推理，效果出人意料。
Causal Reasoning in Large Language Models: A Knowledge Graph Approach
- 基于知识图谱的随机游走，利用因果关系构建推理路径。
- 引入三句看似无关的句子反而提升性能，突破传统认知。
- 适合研究大模型推理机制或因果建模的学者参考。
大型语言模型（LLMs）通常通过检索语义相似信息或借助链式思维等结构化提示来提升性能。尽管两者均被认为至关重要，但尚不清楚哪种策略影响更大，或是否需要结合使用。本文提出一种基于知识图谱（KG）的随机游走推理方法，利用因果关系进行推理。在基于KG的常识问答任务上进行实验，结果表明该方法显著提升了LLM的推理能力与表现。有趣的是，在查询中加入三句看似无关的句子后，模型性能反而提高，违背常规直觉。这些发现表明，将因果结构融入提示中可显著增强推理能力，为优化大模型性能提供了新视角。
原文摘要 · Abstract (English)
Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. While both strategies are considered crucial, it remains unclear which has a greater impact on model performance or whether a combination of both is necessary. This paper answers this question by proposing a knowledge graph (KG)-based random-walk reasoning approach that leverages causal relationships. We conduct experiments on the commonsense question answering task that is based on a KG. The KG inherently provides both relevant information, such as related entity keywords, and a reasoning structure through the connections between nodes. Experimental results show that the proposed KG-based random-walk reasoning method improves the reasoning ability and performance of LLMs. Interestingly, incorporating three seemingly irrelevant sentences into the query using KG-based random-walk reasoning enhances LLM performance, contrary to conventional wisdom. These findings suggest that integrating causal structures into prompts can significantly improve reasoning capabilities, providing new insights into the role of causality in optimizing LLM performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。