Wordnet and word ladders: climbing the abstraction taxonomy with LLMs

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. However, 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.