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

Understand

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Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing fine-grained clinical details essential for accurate medical question answering.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