使用 RAG 技术进行 PubMed 检索。

PubMed Retrieval with RAG Techniques.

机构信息

University of Victoria, USA.

出版信息

Stud Health Technol Inform. 2024 Aug 22;316:652-653. doi: 10.3233/SHTI240498.

Abstract

This study explores the application of Retriever-Augmented Generation (RAG) in enhancing medical information retrieval from the PubMed database. By integrating RAG with Large Language Models (LLMs), we aim to improve the accuracy and relevance of medical information provided to healthcare professionals. Our evaluation on a labeled dataset of 1,000 queries demonstrates promising results in answer relevance, while highlighting areas for improvement in groundedness and context relevance.

摘要

本研究探索了利用检索增强生成（Retriever-Augmented Generation，RAG）来增强从 PubMed 数据库中检索医学信息的应用。通过将 RAG 与大型语言模型（Large Language Models，LLMs）相结合，我们旨在提高向医疗保健专业人员提供的医学信息的准确性和相关性。我们在一个包含 1000 个查询的标注数据集上的评估结果表明，在答案相关性方面取得了有前景的结果，同时也突出了在扎根性和上下文相关性方面需要改进的领域。