SearchRAG：搜索引擎能否助力基于大语言模型的医疗问答？

SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?

February 18, 2025

作者: Yucheng Shi, Tianze Yang, Canyu Chen, Quanzheng Li, Tianming Liu, Xiang Li, Ninghao Liu

cs.AI

摘要

大型语言模型（LLMs）在通用领域展现出了卓越的能力，但在需要专业知识的任务上往往表现欠佳。传统的检索增强生成（RAG）技术通常从静态知识库中检索外部信息，这些信息可能已过时或不完整，缺乏对准确医疗问答至关重要的精细临床细节。本研究中，我们提出了SearchRAG，一种新颖的框架，通过利用实时搜索引擎克服了这些限制。我们的方法采用合成查询生成技术，将复杂的医疗问题转化为适合搜索引擎的查询，并利用基于不确定性的知识选择机制，筛选并整合最相关且信息丰富的医学知识到LLM的输入中。实验结果表明，我们的方法显著提高了医疗问答任务中的回答准确性，特别是在需要详细和最新知识的复杂问题上。

English

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Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing fine-grained clinical details essential for accurate medical question answering.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.