Pushing GPT’s creativity to Its limits: alternative uses and Torrance Tests

In this paper, we investigate the potential of Large Language Models (LLMs), specifically GPT-4, to improve their creative responses in well-known creativity tests, such as Guilford's Alternative Uses Test (AUT) and an adapted version of the Torrance Test of Creative Thinking (TTCT) visual completion tests. We exploit GPT-4's self-improving ability by using a sequence of forceful interactive prompts in a multi-step conversation, aiming to accelerate the convergence process towards more creative responses. Our contributions include an automated approach to enhance GPT's responses in the AUT and TTCT visual completion test and a series of prompts to generate and evaluate GPT's responses in these tests. Our results show that the creativity of GPT's responses can be improved through the use of forceful prompts. This paper opens up possibilities for future research on different sets of prompts to further improve the creativity convergence of LLM-generated responses and the application of similar interactive processes to tasks involving other cognitive skills.