通过大型语言模型和知识图表增强知识的因果发现:在慢性腰部疼痛中的应用.
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
基于知识图的提取增强生成 (GraphRAG) 显著改善了慢性腰痛的因果发现,优于标准RAG和大型语言模型.
科学领域:
- 生物医学研究中的因果推断和人工智能.
背景情况:
- 数据驱动的因果发现受到数据集约束和缺乏外部知识的限制.
- 大型语言模型 (LLM) 和检索增强生成 (RAG) 具有增强因果发现的潜力.
研究的目的:
- 评估基于知识图的RAG (GraphRAG),用于增强慢性腰部疼痛的因果发现.
- 将GraphRAG性能与LLM增强,RAG增强和单独数据的因果发现进行比较.
主要方法:
- 用专家定义的慢性腰痛因果图作为基本真理.
- 实施并对GraphRAG,RAG和LLM增强进行基准测试,以发现因果关系.
- 探索了各种诱导策略来评估因果关系.
主要成果:
- 在增加因果发现方面,GraphRAG获得了最高的F1得分 (0.745).
- 图形RAG的表现优于RAG (0.714),LLM增强 (0.636) 和单独数据发现 (0.396).
结论:
- 在增加因果发现方面,GraphRAG代表了一项重大进步.
- 通过基于图形的RAG集成领域知识可以加速复杂条件的因果建模,如慢性下背痛.
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