Divergent creativity in humans and large language models

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…

• CR
  Canada Research Chairs

  Award: 950-232368
• CU
  Concordia University
• CF
  Courtois Foundation
• FD
  Fonds de Recherche du Québec-Société et Culture

  Award: 274043
• CI
  Canadian Institutes of Health Research
• NS
  Natural Sciences and Engineering Research Council of Canada

  Awards: 2021-03426, 2023-RS6-309472
• SS
  Social Sciences and Humanities Research Council of Canada
• FD
  Fonds de recherche du Québec – Nature et technologies

  Award: 2023-RS6-309472