Work

How Humans and LLMs Organize Conceptual Knowledge: Exploring Subordinate Categories in Italian

Year: 2025

Type: conference-paper

Abstract: People can categorize the same entity at multiple taxonomic levels, such as basic (bear), superordinate (animal), and subordinate (grizzly bear).While prior research has focused on basic-level categories, this study is the first attempt to examine the organization of categories by analyzing exemplars produced at the subordinate level.We present a new Italian psycholinguistic dataset of human-generated exemplars for 187 concrete words.We then use these data to evaluate whether textual and vision LLMs produce meaningful exemplars that align with human category organization across three key tasks: exemplar generation, category induction, and typicality judgment.Our findings show a low alignment between humans and LLMs, consistent with previous studies.However, their performance varies notably across different semantic domains.Ultimately, this study highlights both the promises and the constraints of using AI-generated exemplars to support psychological and linguistic research.1

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-long.224

Authors: Andrea Pedrotti, Giulia Rambelli, Caterina Villani, Marianna Bolognesi

Institutions: Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", University of Bologna

Language: English

Topic: Categorization, perception, and language

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE European Research Council, NextGenerationEU

Awards: Abstraction. Unlocking meaning from experience, through language., G3819771052, G8967762112, G972786147