# Characterising the Creative Process in Humans and Large Language Models

arXiv: https://arxiv.org/abs/2405.00899 · HTML: https://mlsanity.com/paper/2405.00899/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 8 of 8)

- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [What's In My Big Data?](https://mlsanity.com/paper/2310.20707/) (2023)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [Putting GPT-3's Creativity to the (Alternative Uses) Test](https://mlsanity.com/paper/2206.08932/) (2022)
- [MacGyver: Are Large Language Models Creative Problem Solvers?](https://mlsanity.com/paper/2311.09682/) (2023)
- [Can AI Be as Creative as Humans?](https://mlsanity.com/paper/2401.01623/) (2024)

## Built on by (most cited first, 6 of 6)

- [Evaluating Creative Short Story Generation in Humans and Large Language Models](https://mlsanity.com/paper/2411.02316/) (2024)
- [QUDsim: Quantifying Discourse Similarities in LLM-Generated Text](https://mlsanity.com/paper/2504.09373/) (2025)
- [The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks](https://mlsanity.com/paper/2410.07391/) (2024)
- [Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability](https://mlsanity.com/paper/2504.12320/) (2025)
- [Initial Development and Evaluation of the Creative Artificial Intelligence through Recurring Developments and Determinations (CAIRDD) System](https://mlsanity.com/paper/2409.02291/) (2024)
- [Multi-agent AI systems outperform human teams in creativity](https://mlsanity.com/paper/2605.17885/) (2026)

## Abstract (arXiv)

Large language models appear quite creative, often performing on par with the average human on creative tasks. However, research on LLM creativity has focused solely on textitproducts, with little attention on the creative textitprocess. Process analyses of human creativity often require hand-coded categories or exploit response times, which do not apply to LLMs. We provide an automated method to characterise how humans and LLMs explore semantic spaces on the Alternate Uses Task, and contrast with behaviour in a Verbal Fluency Task. We use sentence embeddings to identify response categories and compute semantic similarities, which we use to generate jump profiles. Our results corroborate earlier work in humans reporting both persistent (deep search in few semantic spaces) and flexible (broad search across multiple semantic spaces) pathways to creativity, where both pathways lead to similar creativity scores. LLMs were found to be biased towards either persistent or flexible paths, that varied across tasks. Though LLMs as a population match human profiles, their relationship with creativity is different, where the more flexible models score higher on creativity. Our dataset and scripts are available on hrefhttps://github.com/surabhisnath/CreativeProcessGitHub.