AI RESEARCH
A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
arXiv CS.LG
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ArXi:2604.07274v1 Announce Type: cross Large language models (LLMs) have nstrated strong capabilities in medical question answering; however, purely parametric models often suffer from knowledge gaps and limited factual grounding. Retrieval-augmented generation (RAG) addresses this limitation by integrating external knowledge retrieval into the reasoning process. Despite increasing interest in RAG-based medical systems, the impact of individual retrieval components on performance remains insufficiently understood.