Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs

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. Keywords: Japanese Medical Question Answering RAG Small-Scale LLMs Knowledge Graph. 1 Introduction Large language models (LLMs) have achieved remarkable performance in medical question answering (QA), even demonstrating the ability to pass medical licensing exams (e.g., the United States Medical Licensing Examination, USMLE) [19], which highlights their potential to understand complex medical knowledge. In particular, recent research [23,25,28] has explored the use of retrieval-augmented generation (RAG) [6] to incorporate external medical knowledge into LLMs, effectively mitigating "hallucination" issues [17,29,30] and further enhancing their applicability in medical QA tasks.