AUTHORS: Giovanni Puccetti, Andrea Esuli, Marianna Bolognesi

WORK PACKAGE: WP 8 – UbiQuity

URL: https://github.com/unipv-larl/GWC2025/releases/download/papers/GWC2025_paper_18.pdf

Keywords:

Abstract
WordNet has long served as a benchmark for approximating the mechanisms of semantic categorization in the human mind, particularly through its hierarchical structure of word synsets, most notably the IS-A relation. How ever, these semantic relations have traditionally been curated manually by expert lexicographers, relying on external resources like dictionaries and corpora. In this paper, we explore
whether large language models (LLMs) can be leveraged to approximate these hierarchical semantic relations, potentially offering a scalable and more dynamic alternative for maintaining and updating the WordNet taxonomy.
This investigation addresses the feasibility and implications of automating this process with LLMs by testing a set of prompts encoding different sociodemographic traits and finds that adding age and job information to the prompt affects the model ability to generate text in agreement with hierarchical semantic relations while gender does not have a statistically significant impact.