TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought
This work addresses the limitations of conventional retrieval-augmented generation (RAG) approaches in handling the complex reasoning and individualized variability inherent in traditional Chinese medicine (TCM) syndrome differentiation and treatment. To bridge this gap, the authors propose an enhanced RAG framework that integrates a structured TCM knowledge graph with chain-of-thought (CoT) reasoning, achieving, for the first time, effective alignment between general TCM knowledge and personalized clinical inference. By synergistically combining knowledge graphs, CoT prompting, RAG, and large language models, the proposed method significantly outperforms native large language models, supervised fine-tuned models, and other RAG baselines across multiple TCM datasets. Notably, it substantially improves the performance of non-Chinese large language models on TCM-specific tasks, demonstrating its effectiveness in contextualizing domain-specific reasoning within a linguistically diverse setting.