用知识超图整合医学证据，提升大模型在循证医疗中的生成质量。
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine
- 构建知识超图整合多源医学证据，捕捉复杂关系。
- 在6个数据集上优于现有RAG方法，显著降低幻觉率。
- 适合医疗决策支持、医学问答等需要高可靠性的场景。
循证医学在医疗领域的大语言模型应用中至关重要，为临床决策提供可靠依据。尽管当前检索增强生成技术已有所进展，但仍面临证据分散难以收集、复杂查询下证据组织效率低两大挑战。为此，我们提出利用大语言模型从多源数据中搜集零散证据，并构建基于知识超图的证据管理模型，实现证据融合与复杂关系建模。为进一步支持复杂查询，设计了重要性驱动的证据优先级算法（IDEP），通过大模型生成多个证据特征并赋予重要性评分，据此排序并输出最终检索结果。六组实验表明，该方法在医学测验、幻觉检测和决策支持等循证医学相关任务中均优于现有RAG技术。测试集与构建的知识图谱可访问：https://drive.google.com/rag4ebm。
原文摘要 · Abstract (English)
Evidence-based medicine (EBM) plays a crucial role in the application of large language models (LLMs) in healthcare, as it provides reliable support for medical decision-making processes. Although it benefits from current retrieval-augmented generation~(RAG) technologies, it still faces two significant challenges: the collection of dispersed evidence and the efficient organization of this evidence to support the complex queries necessary for EBM. To tackle these issues, we propose using LLMs to gather scattered evidence from multiple sources and present a knowledge hypergraph-based evidence management model to integrate these evidence while capturing intricate relationships. Furthermore, to better support complex queries, we have developed an Importance-Driven Evidence Prioritization (IDEP) algorithm that utilizes the LLM to generate multiple evidence features, each with an associated importance score, which are then used to rank the evidence and produce the final retrieval results. Experimental results from six datasets demonstrate that our approach outperforms existing RAG techniques in application domains of interest to EBM, such as medical quizzing, hallucination detection, and decision support. Testsets and the constructed knowledge graph can be accessed at \href{https://drive.google.com/file/d/1WJ9QTokK3MdkjEmwuFQxwH96j_Byawj_/view?usp=drive_link}{https://drive.google.com/rag4ebm}.
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