Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers
Measuring the creativity of large language models (LLMs) is essential for designing methods that can improve creativity and for enhancing our scientific understanding of this ability. To accomplish this, it has become common in recent years to administer tests of human creativity to LLMs. Although these tests provide a convenient and fully automated way to score "creativity," their validity as measures of machine creativity has not been established, and these tests already have limited validity as predictors of human creativity. To address this problem, we conduct the first large-scale, systematic study assessing the effectiveness of human creativity tests for predicting the creative achi...
| Authors | Samuel Schapiro, Alexi Gladstone, Jonah Black et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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