# Enhancing Creativity in Large Language Models through Associative Thinking Strategies

arXiv: https://arxiv.org/abs/2405.06715 · HTML: https://mlsanity.com/paper/2405.06715/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 9 May 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

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

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [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)
- [On the Creativity of Large Language Models](https://mlsanity.com/paper/2304.00008/) (2023)

## Built on by (most cited first, 13 of 13)

- [Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications](https://mlsanity.com/paper/2409.16605/) (2024)
- [Creative Preference Optimization](https://mlsanity.com/paper/2505.14442/) (2025)
- [Evaluating Creative Short Story Generation in Humans and Large Language Models](https://mlsanity.com/paper/2411.02316/) (2024)
- [Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems](https://mlsanity.com/paper/2410.18336/) (2024)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation](https://mlsanity.com/paper/2602.09305/) (2026)
- [Probing and Inducing Combinational Creativity in Vision-Language Models](https://mlsanity.com/paper/2504.13120/) (2025)
- [MetaMuse: Algorithm Generation via Creative Ideation](https://mlsanity.com/paper/2510.03851/) (2025)
- [Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination](https://mlsanity.com/paper/2504.20643/) (2025)
- [Inducing Sustained Creativity and Diversity in Large Language Models](https://mlsanity.com/paper/2603.19519/) (2026)
- [Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2603.19087/) (2026)
- [VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs](https://mlsanity.com/paper/2512.12072/) (2025)
- [Playing with Words, Improving with Rewards: Training Language Models for Creative Association](https://mlsanity.com/paper/2605.27832/) (2026)

## Abstract (arXiv)

This paper explores the enhancement of creativity in Large Language Models (LLMs) like vGPT-4 through associative thinking, a cognitive process where creative ideas emerge from linking seemingly unrelated concepts. Associative thinking strategies have been found to effectively help humans boost creativity. However, whether the same strategies can help LLMs become more creative remains under-explored. In this work, we investigate whether prompting LLMs to connect disparate concepts can augment their creative outputs. Focusing on three domains -- Product Design, Storytelling, and Marketing -- we introduce creativity tasks designed to assess vGPT-4's ability to generate original and useful content. By challenging the models to form novel associations, we evaluate the potential of associative thinking to enhance the creative capabilities of LLMs. Our findings show that leveraging associative thinking techniques can significantly improve the originality of vGPT-4's responses.