Enhancing Creativity in Large Language Models through Associative Thinking Strategies
This work addresses the challenge of boosting creativity in LLMs for applications requiring original content generation, though it appears incremental as it applies known human cognitive strategies to LLMs.
This paper tackled the problem of enhancing creativity in Large Language Models (LLMs) like vGPT-4 by using associative thinking strategies, and found that these techniques significantly improved the originality of the model's responses in domains such as Product Design, Storytelling, and Marketing.
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.