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A large-scale comparison of divergent creativity in humans and large language models

Nature Human Behaviour (2025)Cite this article

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Abstract

Human–machine partnerships are increasingly used to address grand societal challenges, yet knowledge of the comparative strengths of humans and machines to innovate is nascent. Here we compare the ability of humans (N = 9,198) and large language models (LLMs, N = 215,542 observations) to generate novel ideas in an established creativity task. We present three key results. First, human creativity on average is slightly higher than that of LLMs. Second, creativity differences are pronounced at the extremes of the distribution, with humans exhibiting greater variability and higher levels of creativity in the right-hand tail of the distribution. Third, attempts to increase the creativity of LLMs through instructing LLMs to take on genius personas or different demographic roles lifted performance up to a threshold beyond which the output became opposite real-life patterns, whereas strategic prompt-engineering efforts yielded mixed to negative results. We discuss the implications of our findings for human–machine collaboration and problem solving.

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Fig. 1: Comparison of the divergent creativity scores between humans and LLMs.
Fig. 2: Comparison of divergent creativity scores across different temperature values for LLMs.
Fig. 3: Comparison of divergent creativity scores across different perspective prompts for LLMs.
Fig. 4: Comparison of divergent creativity scores across different celebrity prompts for LLMs.
Fig. 5: Comparison of divergent creativity scores across different demographic prompts for LLMs.

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Data availability

Data for all analyses in the main manuscript and Supplementary Information are publicly available in the Open Science Framework (https://osf.io/a9v2t).

Code availability

Code for all analyses in the main manuscript and Supplementary Information are publicly available in the Open Science Framework (https://osf.io/a9v2t).

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Acknowledgements

We thank Z. Dai, K. Savani and Z. Shen for their contributions to this work. D.W. was supported by the Seed Fund for Basic Research from the University of Hong Kong (grant no. 2201101303). D.H. was supported by the National Natural Science Foundation of China (grant nos. 72503232, 72574227 and T2293771). H.S. was supported by the Theme-based Research Fund provided by HKU Education Consulting (Shenzhen) Co., Ltd (grant SZRI2023-TBRF-03), the Research Grants Council of the Hong Kong Special Administrative Region, China (grant CRF-C7162-20G), and Strategic allocation 2018/19 (2c): Capacity Building for Development of ‘Business Analytics and Big Data’. B.U. was supported by the National Science Foundation through the NSF National Synthesis Center for Emergence in the Molecular and Cellular Sciences (grant MCB-2335029), Northwestern University’s Kellogg School of Management, Northwestern Institute on Complex Systems, and the Ryan Institute on Complexity. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Faculty of Business and Economics, University of Hong Kong, Hong Kong, China

    Dawei Wang & Haipeng Shen

  2. Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA

    Dawei Wang & Brian Uzzi

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Difang Huang

  4. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China

    Difang Huang

  5. Department of Management and Organizations, Kellogg School of Management, Northwestern University, Evanston, IL, USA

    Brian Uzzi

  6. Ryan Institute of Complexity, Kellogg School of Management, Northwestern University, Evanston, IL, USA

    Brian Uzzi

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  1. Dawei Wang
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Contributions

D.W., D.H., H.S. and B.U. designed the research. D.W. performed the research. D.W. and H.S. analysed the data. D.W., D.H. and B.U. wrote the paper.

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Correspondence to Dawei Wang or Brian Uzzi.

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Nature Human Behaviour thanks Tuhin Chakrabarty, Liuqing Chen and Ken Gilhooly for their contribution to the peer review of this work. Peer reviewer reports are available.

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Wang, D., Huang, D., Shen, H. et al. A large-scale comparison of divergent creativity in humans and large language models. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02331-1

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