Title: A Bilingual Acupuncture Question Answering System via Lightweight LLMs and Retrieval-Augmented Generation
Primary area: foundation or frontier models, including LLMs
Keywords: retrieval-augmented generation, large language models, acupuncture, traditional Chinese medicine, hallucination mitigation, bilingual QA, clinical validation

Abstract: Large language models (LLMs) are prone to hallucinations and often lack reliable access to structured, domain-specific knowledge in Traditional Chinese Medicine (TCM). We present the first bilingual (Chinese--English) acupuncture question answering system built on lightweight LLM backbones and retrieval-augmented generation (RAG). The system integrates a curated ontology covering 361 acupoints and 14 meridians, clinician-authored case records, and a triple-constraint decoding strategy (terminology checking, evidence grounding, and safety filtering) to deliver controlled, verifiable answers. On our evaluation suite, the best-performing configuration (bert+baichuan) achieves 94.4% context recall, 97.2% faithfulness, and 96.1% answer relevance (RAGAS), together with 0.88 BLEU, 0.94 ROUGE, a 94 GPT score, and a 90 expert score. These results confirm that bilingual embedding fusion plus constraint-based decoding substantially improves factuality and clinical usefulness over pure LLM baselines, establishing a strong foundation for reliable and accessible question answering system in acupuncture-specific.

TL;DR: We propose the first bilingual acupuncture QA system that combines lightweight LLMs with retrieval-augmented generation and constraint-based decoding, achieving strong factuality and clinical reliability.