Divergent creativity in humans and large language models.

ABSTRACT: The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs' semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. These divergence-based measures index associative thinking-the ability to access and combine remote concepts in semantic space-an established facet of creative cognition. We benchmark performance on the Divergent Association Task (DAT)

SUBMITTER: Bellemare-Pepin A 

PROVIDER: S-EPMC12824279 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature