SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
  Answering?
Authors: name: Yucheng Shi, name: Tianze Yang, name: Canyu Chen, name: Quanzheng Li, name: Tianming Liu, name: Xiang Li, name: Ninghao Liu

Large Language Models (LLMs) have shown remarkable capabilities in general
domains but often struggle with tasks requiring specialized knowledge.
Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve
external information from static knowledge bases, which can be outdated or
incomplete, missing fine-grained clinical details essential for accurate
medical question answering. In this work, we propose SearchRAG, a novel
framework that overcomes these limitations by leveraging real-time search
engines. Our method employs synthetic query generation to convert complex
medical questions into search-engine-friendly queries and utilizes
uncertainty-based knowledge selection to filter and incorporate the most
relevant and informative medical knowledge into the LLM's input. Experimental
results demonstrate that our method significantly improves response accuracy in
medical question answering tasks, particularly for complex questions requiring
detailed and up-to-date knowledge.