用大模型生成可解释推理路径，融合内外知识提升问答准确性
KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs
- 让大模型引导搜索知识图谱中的可解释推理路径
- 在多个数据集上显著降低幻觉，准确率最高提升12.3%
- 适合需要可信推理的医疗、金融等专业场景
大型语言模型（LLMs）在复杂任务中表现出色，但仍存在幻觉问题。通过引入外部知识（如知识图谱，KGs），可提升其提供事实性答案的能力，具有重要应用价值。然而现有方法存在三大局限：内部知识挖掘不足、可解释推理路径生成受限、内外知识融合不清晰。为此，我们提出KnowPath框架，通过大模型内部知识引导外部知识图谱中可解释有向子图的探索，更有效地融合两类知识以实现更精准推理。在多个真实世界数据集上的大量实验验证了该方法的有效性。代码与数据已开源。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated remarkable capabilities in various complex tasks, yet they still suffer from hallucinations. By incorporating and exploring external knowledge, such as knowledge graphs(KGs), LLM's ability to provide factual answers has been enhanced. This approach carries significant practical implications. However, existing methods suffer from three key limitations: insufficient mining of LLMs' internal knowledge, constrained generation of interpretable reasoning paths, and unclear fusion of internal and external knowledge. Therefore, we propose KnowPath, a knowledge-enhanced large model framework driven by the collaboration of internal and external knowledge. It relies on the internal knowledge of the LLM to guide the exploration of interpretable directed subgraphs in external knowledge graphs, better integrating the two knowledge sources for more accurate reasoning. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of KnowPath. Our code and data are available at https://github.com/tize-72/KnowPath.
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。