用医学本体和大模型增强生物医学检索，效果显著提升。
Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models
- 结合医学本体与大模型生成，动态扩展查询词
- 在三个数据集上最高提升22.1%的检索效果
- 抗查询扰动能力强，幻觉更少，适合临床研究
在大型生物医学文献库上实现高效问答，依赖于有效的文档检索技术。由于领域专用术语和用户查询的语义模糊性，该任务仍具挑战性。本文提出BMQExpander，一种基于本体的查询扩展流程，融合UMLS Metathesaurus中的医学知识（定义与关系）与大语言模型的生成能力，以提升检索效果。我们实现了多种先进基线，包括稀疏与稠密检索器、查询扩展方法及生物医学专用方案。结果表明，BMQExpander在NFCorpus、TREC-COVID和SciFact三个主流生物医学信息检索基准上表现优异，相比稀疏基线最高提升22.1%（NDCG@10），相比最强基线最高提升6.5%。此外，在查询扰动设置下，其泛化能力优于监督基线，最高提升达15.7%。作为附加贡献，我们公开了改写后的评测数据集。定性分析显示，相较其他基于LLM的查询扩展方法，其幻觉更少。
原文摘要 · Abstract (English)
Effective Question Answering (QA) on large biomedical document collections requires effective document retrieval techniques. The latter remains a challenging task due to the domain-specific vocabulary and semantic ambiguity in user queries. We propose BMQExpander, a novel ontology-aware query expansion pipeline that combines medical knowledge - definitions and relationships - from the UMLS Metathesaurus with the generative capabilities of large language models (LLMs) to enhance retrieval effectiveness. We implemented several state-of-the-art baselines, including sparse and dense retrievers, query expansion methods, and biomedical-specific solutions. We show that BMQExpander has superior retrieval performance on three popular biomedical Information Retrieval (IR) benchmarks: NFCorpus, TREC-COVID, and SciFact - with improvements of up to 22.1% in NDCG@10 over sparse baselines and up to 6.5% over the strongest baseline. Further, BMQExpander generalizes robustly under query perturbation settings, in contrast to supervised baselines, achieving up to 15.7% improvement over the strongest baseline. As a side contribution, we publish our paraphrased benchmarks. Finally, our qualitative analysis shows that BMQExpander has fewer hallucinations compared to other LLM-based query expansion baselines.
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