# Divergent Creativity in Humans and Large Language Models

arXiv: https://arxiv.org/abs/2405.13012 · HTML: https://mlsanity.com/paper/2405.13012/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 86 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Personality Traits in Large Language Models](https://mlsanity.com/paper/2307.00184/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Putting GPT-3's Creativity to the (Alternative Uses) Test](https://mlsanity.com/paper/2206.08932/) (2022)
- [Assessing and Understanding Creativity in Large Language Models](https://mlsanity.com/paper/2401.12491/) (2024)
- [MacGyver: Are Large Language Models Creative Problem Solvers?](https://mlsanity.com/paper/2311.09682/) (2023)
- [Probing the Creativity of Large Language Models: Can models produce divergent semantic association?](https://mlsanity.com/paper/2310.11158/) (2023)
- [Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers](https://mlsanity.com/paper/2309.12570/) (2023)
- [Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity](https://mlsanity.com/paper/2303.12003/) (2023)
- [Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity](https://mlsanity.com/paper/2302.12832/) (2023)
- [Analyzing biological and artificial neural networks: challenges with opportunities for synergy?](https://mlsanity.com/paper/1810.13373/) (2018)
- [Human in the Loop for Machine Creativity](https://mlsanity.com/paper/2110.03569/) (2021)
- [The Shifting Sands of Creative Thinking: Connections to Dual Process Theory](https://mlsanity.com/paper/1409.2207/) (2014)
- [Modeling documents with Generative Adversarial Networks](https://mlsanity.com/paper/1612.09122/) (2016)

## Built on by (most cited first, 30 of 31)

- [Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?](https://mlsanity.com/paper/2407.01119/) (2024)
- [Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery](https://mlsanity.com/paper/2605.08956/) (2026)
- [Measuring Combinatorial Creativity in Large Language Models and Humans](https://mlsanity.com/paper/2509.21043/) (2025)
- [Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability](https://mlsanity.com/paper/2504.12320/) (2025)
- [Probing and Inducing Combinational Creativity in Vision-Language Models](https://mlsanity.com/paper/2504.13120/) (2025)
- [GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art](https://mlsanity.com/paper/2505.11436/) (2025)
- [Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists](https://mlsanity.com/paper/2501.12374/) (2025)
- [Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism](https://mlsanity.com/paper/2510.13170/) (2025)
- [Machine-Assisted Grading of Nationwide School-Leaving Essay Exams with LLMs and Statistical NLP](https://mlsanity.com/paper/2601.16314/) (2026)
- [The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning](https://mlsanity.com/paper/2605.01704/) (2026)
- [Are LLMs becoming similarly creative? Evidence from three years of models](https://mlsanity.com/paper/2608.19437/) (2026)
- [Reasoning Beyond the Obvious: Evaluating Divergent and Convergent Thinking in LLMs for Financial Scenarios](https://mlsanity.com/paper/2507.18368/) (2025)
- [Group Selection as a Safeguard Against AI Substitution](https://mlsanity.com/paper/2602.03541/) (2026)
- [Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2603.19087/) (2026)
- [Can LLM Agents Discover? Evaluating Creativity on ML Engineering Tasks](https://mlsanity.com/paper/2608.30047/) (2026)
- [Initial Development and Evaluation of the Creative Artificial Intelligence through Recurring Developments and Determinations (CAIRDD) System](https://mlsanity.com/paper/2409.02291/) (2024)
- [Knowledge Conceptualization Impacts RAG Efficacy](https://mlsanity.com/paper/2507.09389/) (2025)
- [AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity](https://mlsanity.com/paper/2509.14171/) (2025)
- [MazeMate: An LLM-Powered Chatbot to Support Computational Thinking in Gamified Programming Learning](https://mlsanity.com/paper/2511.03727/) (2025)
- [Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets](https://mlsanity.com/paper/2511.09454/) (2025)
- [Stable diffusion models reveal a persisting human and AI gap in visual creativity](https://mlsanity.com/paper/2511.16814/) (2025)
- [CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge](https://mlsanity.com/paper/2604.03374/) (2026)
- [Large Language Models Align with the Human Brain during Creative Thinking](https://mlsanity.com/paper/2604.03480/) (2026)
- [Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection](https://mlsanity.com/paper/2605.28664/) (2026)
- [IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs](https://mlsanity.com/paper/2606.00875/) (2026)
- [AGC-Bench: Measuring Artificial General Creativity](https://mlsanity.com/paper/2607.01152/) (2026)
- [Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation](https://mlsanity.com/paper/2608.05576/) (2026)
- [Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding](https://mlsanity.com/paper/2608.06501/) (2026)
- [Using Poly-Encoders for Computationally Efficient Automated Creativity Assessment](https://mlsanity.com/paper/2608.26165/) (2026)
- [Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn](https://mlsanity.com/paper/2610.12792/) (2026)

## Abstract (arXiv)

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs' semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence that LLMs can surpass average human performance on the Divergent Association Task, and approach human creative writing abilities, though they fall short of the typical performance of highly creative humans. Notably, even the top performing LLMs are still largely surpassed by highly creative individuals, underscoring a ceiling that current LLMs still fail to surpass. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labour by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.