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

被引:7
作者
Formanek, Matus [1 ,2 ]
机构
[1] Univ Zilina, Zilina, Slovakia
[2] Univ Zilina, Fac Humanities, Univ 8215-1, Zilina 01026, Slovakia
关键词
ChatGPT; generative artificial intelligence; large language models; Library and Information Science; use cases;
D O I
10.1177/09610006241241066
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
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.
引用
收藏
页码:568 / 590
页数:23
相关论文
共 35 条
[1]   Persistent Anti-Muslim Bias in Large Language Models [J].
Abid, Abubakar ;
Farooqi, Maheen ;
Zou, James .
AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, 2021, :298-306
[2]   On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? [J].
Bender, Emily M. ;
Gebru, Timnit ;
McMillan-Major, Angelina ;
Shmitchell, Shmargaret .
PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, 2021, :610-623
[3]   Sustainable maize production and climatic change in Nepal: robust role of climatic and non-climatic factors in the long-run and short-run [J].
Chandio, Abbas Ali ;
Akram, Waqar ;
Bashir, Uzma ;
Ahmad, Fayyaz ;
Adeel, Sultan ;
Jiang, Yuansheng .
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2023, 25 (02) :1614-1644
[4]  
chatPDF, FAQ
[5]  
Chen Y., 2023, CHATGPT BASED MODEL
[6]  
Cordell Ryan, 2020, Machine Learning + Libraries: A Report on the State of the Field
[7]  
Coursera, 2023, WHAT IS PROMPT ENG D
[8]  
DAIR.AI, 2023, RETRIEVAL AUGMENTED
[9]  
dair.ai, 2023, Prompt Engineering Guide
[10]  
Dehouche N., 2021, ETHICS SCI ENV POLIT, V21, P17, DOI [DOI 10.3354/ESEP00195, 10.3354/ESEP00195]