SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
  Answering?
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external information from static knowledge bases, which can be outdated or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.
67b689aeba514d2c2c969289
Yucheng Shi
claimed_verified
2025-02-20T09:36:18.925Z
64beb6b6140491ca9f803ebf
/avatars/0daa2e813a13668b8b708cd8c12763d9.svg
Yucheng SHi
user
YuchengShi
67b689aeba514d2c2c96928a
Tianze Yang
67b689aeba514d2c2c96928b
Canyu Chen
admin_assigned
2025-02-20T16:58:09.392Z
6483af58571c2dcfa98cae82
https://cdn-avatars.huggingface.co/v1/production/uploads/6483af58571c2dcfa98cae82/d8Vnh_EG1KxeEoNAAQXHZ.jpeg
Canyu Chen
user
canyuchen
67b689aeba514d2c2c96928c
Quanzheng Li
67b689aeba514d2c2c96928d
Tianming Liu
67b689aeba514d2c2c96928e
Xiang Li
67b689aeba514d2c2c96928f
Ninghao Liu
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
  Answering?
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external information from static knowledge bases, which can be outdated or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.
SearchRAG framework enhances medical question answering by using real-time search engines and uncertainty-based selection to integrate current and detailed knowledge into LLM inputs.
Large Language Models Retrieval-Augmented Generation RAG static knowledge bases real-time search engines synthetic query generation uncertainty-based knowledge selection medical question answering