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cs.CL2025
Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs
Yingjian Chen, Feiyang Li, Xingyu Song +5
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 model…
cs.CL2025★ 1 cited
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models
Feiyang Li, Peng Fang, Zhan Shi +5
Chain-of-thought (CoT) reasoning boosts large language models' (LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-g…
cs.CL2025
MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering
Feiyang Li, Yingjian Chen, Haoran Liu +10
Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced m…