Large language models (LLMs) perform well in medical QA, but their effectiveness in Japanese contexts is limited due to privacy constraints that prevent the use of commercial models like GPT-4 in clinical settings. As a result, recent efforts focus on instruction-tuning open-source LLMs, though the potential of combining them with retrieval-augmented generation (RAG) remains underexplored. To bridge this gap, we are the first to explore a knowledge graph-based (KG) RAG framework for Japanese medical QA small-scale open-source LLMs. Experimental results show that KG-based RAG has only a limited impact on Japanese medical QA using small-scale open-source LLMs. Further case studies reveal that the effectiveness of the RAG is sensitive to the quality and relevance of the external retrieved content. These findings offer valuable insights into the challenges and potential of applying RAG in Japanese medical QA, while also serving as a reference for other low-resource languages.

本研究解决了日本医学问答中小规模开放源代码LLM的知识共享不足问题。文章首次探讨了基于知识图谱的检索增强生成（RAG）框架，实验证明其对小规模开放源LLM在日本医学问答中的影响有限，但揭示了外部检索内容的质量和相关性对RAG效果的敏感性。这些发现为在低资源语言中应用RAG提供了宝贵的见解。

探索基于知识图谱的RAG在日本医学问答中与小规模LLM的结合