From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering

Medical question answering (QA) demands reliable access to rich, domain-specific knowledge. A prevalent strategy enhances large language models (LLMs) with either external knowledge retrieved from medical corpora or parametric knowledge embedded with model parameters. Existing methods generally fall into two paradigms: Retrieval-Augmented Generation (RAG), which grounds reasoning in retrieved evidence, and Generation-Augmented Generation (GAG), which relies exclusively on internally generated contexts. However, RAG often suffers from noisy or incomplete retrieval, whereas GAG is prone to hallucinated or inaccurate content due to unconstrained generation. Both limitations can misguide reasoning and compromise answer reliability. To overcome these challenges, we propose MedRGAG, a unified retrieval--generation augmented framework that seamlessly integrates external and parametric knowledge for medical QA. MedRGAG consists of two core modules: Knowledge-Guided Context Completion (KGCC), which steers the generator to produce background documents that complement knowledge gaps revealed by retrieval, and Knowledge-Aware Document Selection (KADS), which adaptively selects an optimal combination of retrieved and generated documents to construct concise yet comprehensive evidence for answers. Extensive experiments on five medical QA benchmarks demonstrate that MedRGAG achieves a 12.5% improvement over MedRAG and a 4.5% gain over MedGENIE, highlighting the effectiveness of unifying retrieval and generation for knowledge-intensive medical reasoning. Further analyses show that our framework effectively generates complementary contexts and recovers informative retrieved evidence. Our code and data are publicly available at https://github.com/ll0ruc/MedRGAG.