# Large Language Models show both individual and collective creativity comparable to humans

arXiv: https://arxiv.org/abs/2412.03151 · HTML: https://mlsanity.com/paper/2412.03151/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023)
- [Probing the Creativity of Large Language Models: Can models produce divergent semantic association?](https://mlsanity.com/paper/2310.11158/) (2023)
- [Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity](https://mlsanity.com/paper/2303.12003/) (2023)
- [Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages](https://mlsanity.com/paper/2308.13479/) (2023)

## Built on by (most cited first, 11 of 11)

- [What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models](https://mlsanity.com/paper/2510.04226/) (2025)
- [Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator](https://mlsanity.com/paper/2505.19236/) (2025)
- [Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability](https://mlsanity.com/paper/2504.12320/) (2025)
- [Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis](https://mlsanity.com/paper/2505.17241/) (2025)
- [Post-training makes large language models less human-like](https://mlsanity.com/paper/2605.07632/) (2026)
- [MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models](https://mlsanity.com/paper/2603.00048/) (2026)
- [Automated Creativity Evaluation of Language Models Across Open-Ended Tasks](https://mlsanity.com/paper/2606.11762/) (2026)
- [Why can't Epidemiology be automated (yet)?](https://mlsanity.com/paper/2507.15617/) (2025)
- [Assessing mentalization in humans and large language models](https://mlsanity.com/paper/2608.26291/) (2026)
- [Mapping the Emerging Social Science of Large Language Models](https://mlsanity.com/paper/2609.07598/) (2026)
- [Evaluating LLM Creativity through Long-Tail Performance](https://mlsanity.com/paper/2610.12998/) (2026)

## Abstract (arXiv)

Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to humans? To measure the creativity of LLMs holistically, the current study uses 13 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 52nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. When questioned 10 times, an LLM's collective creativity is equivalent to 8-10 humans. When more responses are requested, two additional responses of LLMs equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work.