【摘要】 大语言模型（LLM）具有强大的学习和推理能力,然而由于缺乏内部知识,该模型面临幻觉挑战。针对医学问答对高质量专业知识的需求,本研究提出一种大语言模型融合知识图谱的医学问答方法,并将其应用于医学科技信息知识服务平台（MedKaaS）的问答系统构建。研究表明,相比传统的知识库问答（KBQA）和新兴的基于生成式大语言模型的问答,本文所提出的大语言模型融合知识图谱的方法充分综合了二者的优势,并为医学问答提供了证据溯源支持,有效提升了医学智能问答质量和医学知识服务效果。更多还原

【Abstract】 Large language modeling(LLM) has powerful learning and reasoning ability, but due to the lack of internal knowledge, the model faces hallucination challenges. To address the need for high-quality professional knowledge in medical Q&A, this study proposes a medical Q&A method by integrating LLM with knowledge graph,and applies it to Q&A system construction of Medical Science and Technology Information Knowledge Service Platform(MedKaaS). The study shows that compared with the traditional knowledge base Q&A(KBQA) and the emerging Q&A based on generative LLM, the method of integrating LLM with knowledge graph proposed in this paper can fully synthesize the advantages of both, provide evidence traceability support for medical Q&A, and effectively improve the quality of medical intelligent Q&A and the effect of medical knowledge service.更多还原