Exploring the potential of large language models and generative artificial intelligence (GPT): Applications in Library and Information Science

IF 1.4 4区 管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE
Matus Formanek
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引用次数: 0

Abstract

The presented study offers a systematic overview of the potential application of large language models (LLMs) and generative artificial intelligence tools, notably the GPT model and the ChatGPT interface, within the realm of library and information science (LIS). The paper supplements and extends the outcomes of a comprehensive information survey on the subject matter with the author’s own experiences and examples showcasing possible applications, demonstrated through illustrative instances. This study does not involve testing available LLMs or selecting the most suitable tool; instead, it targets information professionals, specialists, librarians, and scientists, aiming to inspire them in various ways. Within this paper, we explore both well-known and less recognized use cases of generative AI tools, which may prove relevant not only for the target group of information specialists but also for other users. Our analysis demonstrates that apart from merely summarizing or expanding existing textual content, these AI tools hold the potential for performing non-standard yet sophisticated tasks with electronic information resources. They can facilitate interactive engagement with these resources, aid in the extraction and composition of descriptive metadata, indexing, and even possible classification. Nevertheless, it is essential to acknowledge the numerous limitations of current LLMs, which we acknowledge in this study.
探索大型语言模型和生成式人工智能 (GPT) 的潜力:在图书馆和信息科学中的应用
本研究报告系统地概述了大型语言模型(LLM)和生成式人工智能工具,特别是 GPT 模型和 ChatGPT 界面在图书馆与信息科学(LIS)领域的潜在应用。本文补充并扩展了有关该主题的综合信息调查的成果,作者以自己的经验和实例展示了可能的应用,并通过举例说明进行了演示。这项研究并不涉及测试现有的 LLM 或选择最合适的工具;相反,它以信息专业人员、专家、图书馆员和科学家为目标,旨在以各种方式启发他们。在本文中,我们探讨了生成式人工智能工具的知名和不太知名的使用案例,这些案例可能不仅与信息专家这一目标群体相关,也与其他用户相关。我们的分析表明,除了总结或扩展现有文本内容外,这些人工智能工具还具有利用电子信息资源执行非标准但复杂任务的潜力。它们可以促进与这些资源的互动,帮助提取和组成描述性元数据、编制索引,甚至可能进行分类。然而,我们必须承认当前的 LLM 存在诸多局限性,我们在本研究中也承认了这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Librarianship and Information Science
Journal of Librarianship and Information Science INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
4.70
自引率
11.80%
发文量
82
期刊介绍: Journal of Librarianship and Information Science is the peer-reviewed international quarterly journal for librarians, information scientists, specialists, managers and educators interested in keeping up to date with the most recent issues and developments in the field. The Journal provides a forumfor the publication of research and practical developments as well as for discussion papers and viewpoints on topical concerns in a profession facing many challenges.
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