数字图书馆论坛2025，Vol.21Issue(7)：13-19,7.DOI:10.3772/j.issn.1673-2286.2025.07.002
大语言模型驱动的图书馆书目检索系统研究
LLM-Driven Bibliographic Retrieval System for Libraries:A Case Study of"Ba Jin Thematic Exhibition"Curation Task
摘要
Abstract
This paper addresses long-standing issues in traditional bibliographic retrieval—namely insufficient semantic understanding,difficulty in cross-source integration,and limited result usability—and proposes a large language model(LLM)-driven solution that is retrieval-first with built-in semantic expansion and retrieval enhancement.Methodologically,we employ retrieval-augmented thought(RAT)to plan structured search queries and interface with Online Public Access Catalogue(OPAC)/holdings systems to obtain verifiable,structured bibliographic data.Using the"Ba Jin Thematic Exhibition"as a curation scenario,we design multi-angle retrieval tasks and conduct an empirical evaluation from the perspective of professional users.The experimental results show that the approach significantly improves relevance,coverage,and structural completeness for complex natural-language queries and multi-dimensional information needs,and,during results organization,enhances the practicality of curation through semantic expansion and re-ranking.Building on RAT-based semantic expansion in tandem with OPAC integration,we provide an implementable workflow,offering reusable methods for the design and deployment of generative bibliographic retrieval systems in libraries.关键词
大语言模型/书目检索/信息整合/检索增强思维/图书馆/策展Key words
Large Language Model/Bibliographic Retrieval/Information Integration/Retrieval-Augmented Thought/Library/Curation引用本文复制引用
郭利敏,付雅明,刘悦如..大语言模型驱动的图书馆书目检索系统研究[J].数字图书馆论坛,2025,21(7):13-19,7.基金项目
本研究得到上海市白玉兰人才计划浦江项目"基于生成式人工智能的文化遗产知识服务研究"(编号:24PJC036)、上海图书馆(上海科学技术情报研究所)"2151"人力资源能力建设工程骨干英才项目"多模态系统在图书馆的应用研究"资助. （编号:24PJC036）
