title: SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
    Answering?
summary: 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  external information from static knowledge bases, which can be outdated or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.
paper_authors._id: 67b689aeba514d2c2c969289
paper_authors.name: Yucheng Shi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T09:36:18.925Z
paper_authors._id: 64beb6b6140491ca9f803ebf
paper_authors.avatarUrl: /avatars/0daa2e813a13668b8b708cd8c12763d9.svg
paper_authors.fullname: Yucheng SHi
paper_authors.type: user
paper_authors.user: YuchengShi
paper_authors._id: 67b689aeba514d2c2c96928a
paper_authors.name: Tianze Yang
paper_authors._id: 67b689aeba514d2c2c96928b
paper_authors.name: Canyu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T16:58:09.392Z
paper_authors._id: 6483af58571c2dcfa98cae82
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6483af58571c2dcfa98cae82/d8Vnh_EG1KxeEoNAAQXHZ.jpeg
paper_authors.fullname: Canyu Chen
paper_authors.type: user
paper_authors.user: canyuchen
paper_authors._id: 67b689aeba514d2c2c96928c
paper_authors.name: Quanzheng Li
paper_authors._id: 67b689aeba514d2c2c96928d
paper_authors.name: Tianming Liu
paper_authors._id: 67b689aeba514d2c2c96928e
paper_authors.name: Xiang Li
paper_authors._id: 67b689aeba514d2c2c96928f
paper_authors.name: Ninghao Liu
paper_title: SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
    Answering?
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) knowledge bases SearchRAG synthetic query generation uncertainty-based knowledge selection