Large language models show both individual and collective creativity comparable to humans

• Across 13 creative tasks, the LLMs rank in the 46th percentile against humans. • LLMs excel in divergent thinking and problem solving but lag in creative writing. • When questioned 10 times, an LLM’s collective creativity equals 8-10 humans. • LLMs may compete with a small group of humans in terms of collective creativity. Artificial intelligence, especially large language models (LLMs) are increasingly adopted in the workplace, which has significant implications for the future of work if they show creativity comparable to humans. To measure the creativity of LLMs holistically, the current study uses thirteen creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take a novel approach by comparing them to the collective creativity of groups of humans. We find that the best LLMs (Claude and GPT-4) rank in the 52 nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. We also show that the collective creativity in 10 LLM responses is equivalent to 8-10 humans. When there are more than 10 LLM responses, in terms of incremental collective creativity, two additional LLM responses equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work.

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Publication Details

Journal
Thinking Skills and Creativity
Published
2025-05-14
DOI
https://doi.org/10.1016/j.tsc.2025.101870
Citations
7
Primary Topic
Creativity in Education and Neuroscience
Type
article
Field-Weighted Citation Impact
12.71

Funders

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article

Large language models show both individual and collective creativity comparable to humans

David Stillwell, Xiting Wang, Yanyan Li, Hao Zhang et al.
7 citations
Thinking Skills and Creativity
Creativity in Education and Neuroscience
12.71
article

Large language models show both individual and collective creativity comparable to humans

David Stillwell, Xiting Wang, Yanyan Li, Hao Zhang, Fang Luo, Xing Xie, Yuan Yao, Luning Sun, Yuzhuo Yuan
article en
7 citations

Abstract

• Across 13 creative tasks, the LLMs rank in the 46th percentile against humans. • LLMs excel in divergent thinking and problem solving but lag in creative writing. • When questioned 10 times, an LLM’s collective creativity equals 8-10 humans. • LLMs may compete with a small group of humans in terms of collective creativity. Artificial intelligence, especially large language models (LLMs) are increasingly adopted in the workplace, which has significant implications for the future of work if they show creativity comparable to humans. To measure the creativity of LLMs holistically, the current study uses thirteen creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take a novel approach by comparing them to the collective creativity of groups of humans. We find that the best LLMs (Claude and GPT-4) rank in the 52 nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. We also show that the collective creativity in 10 LLM responses is equivalent to 8-10 humans. When there are more than 10 LLM responses, in terms of incremental collective creativity, two additional LLM responses equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work.

Thinking Skills and CreativityVol. 57
University of Cambridge (GB), Beijing Normal University (CN), Microsoft Research Asia (China) (CN)
National Natural Science Foundation of China
Reduced inequalities
Openalex Percentile: Top 1%
Creativity in Education and Neuroscience
12.71
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