MedBioRAG: 基于大语言模型的语义搜索与检索增强生成用于医学和生物学问答
MedBioRAG: Semantic Search and Retrieval-Augmented Generation with Large Language Models for Medical and Biological QA
- Seonok Kim（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
MedBioRAG通过结合语义搜索、文档检索和监督微调，提升医学和生物学问答任务的性能。
AI中文摘要：
最近的检索增强生成（RAG）进展显著增强了大型语言模型（LLMs）执行复杂问答（QA）任务的能力。在本文中，我们介绍了MedBioRAG，一种检索增强模型，旨在通过结合语义和词汇搜索、文档检索和监督微调来提高生物医学QA性能。MedBioRAG能够高效检索并排序相关生物医学文档，从而实现精确且上下文感知的回答生成。我们使用NFCorpus、TREC-COVID、MedQA、PubMedQA和BioASQ等基准数据集，在文本检索、封闭式QA和长文本QA任务上评估MedBioRAG。实验结果表明，MedBioRAG在所有评估任务中均优于先前的最先进（SoTA）模型和GPT-4o基础模型。值得注意的是，我们的方法在文档检索的NDCG和MRR得分上有所提高，同时在封闭式QA的准确性以及长文本QA的ROUGE得分上也取得更高的成绩。我们的发现突显了基于语义搜索的检索和LLM微调在生物医学应用中的有效性。
英文摘要：
Recent advancements in retrieval-augmented generation (RAG) have significantly enhanced the ability of large language models (LLMs) to perform complex question-answering (QA) tasks. In this paper, we introduce MedBioRAG, a retrieval-augmented model designed to improve biomedical QA performance through a combination of semantic and lexical search, document retrieval, and supervised fine-tuning. MedBioRAG efficiently retrieves and ranks relevant biomedical documents, enabling precise and context-aware response generation. We evaluate MedBioRAG across text retrieval, close-ended QA, and long-form QA tasks using benchmark datasets such as NFCorpus, TREC-COVID, MedQA, PubMedQA, and BioASQ. Experimental results demonstrate that MedBioRAG outperforms previous state-of-the-art (SoTA) models and the GPT-4o base model in all evaluated tasks. Notably, our approach improves NDCG and MRR scores for document retrieval, while achieving higher accuracy in close-ended QA and ROUGE scores in long-form QA. Our findings highlight the effectiveness of semantic search-based retrieval and LLM fine-tuning in biomedical applications.
