Conceptualizations of Information Science by Large Language Models
DOI:
https://doi.org/10.29173/cais1874Keywords:
Large language models, LLMs, Information Science, Large Language Models, Domain AnalysisAbstract
This paper reports a comparative study of the ways in which large language models understand and represent the domain of information science. Five large language models were selected for this study, namely ChatGPT, Perplexity.ai, Google Gemini, Meta AI and Claude. A set of five prompts were utilized in this study for comparison. The findings suggest differences and variations in how these LLMs conceptualize and represent information science, its definitions and interdisciplinarity, theoretical models, and methods.
Conceptualisations des sciences de l'information par les grands modèles de language
Résumé
Cet article porte sur une étude comparative étayant comment les grands modèles de langage comprennent et représentent le domaine des sciences de l'information. Cinq grands modèles de langage ont été sélectionnés pour cette étude, c'est-à-dire ChatGPT, Perplexity.ai, Google Gemini, Meta AI et Claude. Un ensemble de cinq instructions ont été utilisés pour la comparaison au sein de cette étude. Les résultats suggèrent des différences et des variations par rapport à comment ces grands modèles de langage conceptualisent et représentent les sciences de l'information et ses définitions, ainsi que l'interdisciplinarité, les modèles théoriques et les méthodes.
Mots-Clés
Grands modèles de langage; GML; Sciences de l'information; Analyse de domaine
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