Data Governance and Application Development of LargeLanguage Models in Library and Information Services

Abstract

Abstract:

This article primarily discusses the data governance requirements and development patternsof large language models in the field of library and information science. Large language models rely onmassive amounts of text data for unsupervised pre-training and supervised fine-tuning. Domain-specificlarge models, on the other hand, are models that have been fine-tuned on domain-specific data to possessdomain knowledge and solve domain-specific problems to meet the needs of domain applications.The article aims to explore how to better apply generative artificial intelligence to libraries and relatedindustries to promote the development of smart libraries and provide the driving force for high-qualityservices. The article first reviews the breakthrough progress of generative artificial intelligence, and thenintroduces the basic principles and current applications of large models, as well as analyzes the datafactors and data requirements behind the various task capabilities of large models. Finally, the articlediscusses the application potential and development patterns of domain-specific large models. The maincontribution of this article is to analyze the application patterns and data governance of large models inthe field of library and information services, providing a theoretical basis and practical guidance for theapplication of generative artificial intelligence technology in the library industry. At the same time, it alsodiscusses the issues and limitations that need to be considered when applying and evaluating industryspecificlarge models.

Liu Qianqian, Liu Shengying, Liu Wei( Shanghai Library, East China Normal University Library）. Data Governance and Application Development of LargeLanguage Models in Library and Information Services[J]. Libraly Journal, 2023, 42(392): 22-35.

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