SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929719944937472 |
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| author | Shi, Yucheng Yang, Tianze Chen, Canyu Li, Quanzheng Liu, Tianming Li, Xiang Liu, Ninghao |
| author_facet | Shi, Yucheng Yang, Tianze Chen, Canyu Li, Quanzheng Liu, Tianming Li, Xiang Liu, Ninghao |
| contents | 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Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing fine-grained clinical details essential for accurate medical question answering.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering? Shi, Yucheng Yang, Tianze Chen, Canyu Li, Quanzheng Liu, Tianming Li, Xiang Liu, Ninghao Computation and Language Artificial Intelligence Information Retrieval Information Theory 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. |
| title | SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering? |
| topic | Computation and Language Artificial Intelligence Information Retrieval Information Theory |
| url | https://arxiv.org/abs/2502.13233 |