Traceable Cross-Source RAG for Chinese Tibetan Medicine Question Answering
This addresses a domain-specific challenge in medical QA by improving traceability and reducing hallucinations for practitioners, though it is incremental as it builds on existing RAG techniques.
The paper tackled the problem of retrieval-augmented generation (RAG) in Chinese Tibetan medicine, where multiple heterogeneous knowledge bases cause retrieval bias, and proposed methods to improve traceability and cross-KB evidence coverage, resulting in the best CrossEv@5 score while maintaining strong faithfulness and citation correctness.
Retrieval-augmented generation (RAG) promises grounded question answering, yet domain settings with multiple heterogeneous knowledge bases (KBs) remain challenging. In Chinese Tibetan medicine, encyclopedia entries are often dense and easy to match, which can dominate retrieval even when classics or clinical papers provide more authoritative evidence. We study a practical setting with three KBs (encyclopedia, classics, and clinical papers) and a 500-query benchmark (cutoff $K{=}5$) covering both single-KB and cross-KB questions. We propose two complementary methods to improve traceability, reduce hallucinations, and enable cross-KB verification. First, DAKS performs KB routing and budgeted retrieval to mitigate density-driven bias and to prioritize authoritative sources when appropriate. Second, we use an alignment graph to guide evidence fusion and coverage-aware packing, improving cross-KB evidence coverage without relying on naive concatenation. All answers are generated by a lightweight generator, \textsc{openPangu-Embedded-7B}. Experiments show consistent gains in routing quality and cross-KB evidence coverage, with the full system achieving the best CrossEv@5 while maintaining strong faithfulness and citation correctness.