Enhancing Creativity in Large Language Models through Associative Thinking Strategies

TL;DR

This paper investigates how associative thinking strategies can be used to enhance the creativity of large language models like vGPT-4 across domains such as product design, storytelling, and marketing, leading to more original outputs.

Contribution

It introduces a novel approach of applying associative thinking prompts to LLMs to improve their creative output, an area previously under-explored.

Findings

Associative thinking prompts significantly increase originality in LLM responses.

The approach improves creative output across multiple domains.

LLMs can be guided to form novel associations to enhance creativity.

Abstract

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.