Art or Artifice? Large Language Models and the False Promise of Creativity

Tuhin Chakrabarty, Philippe Laban, Divyansh Agarwal, Smaranda Muresan, Chien-Sheng Wu

Abstract:

Researchers have argued that large language models (LLMs) exhibit high-quality writing capabilities from blogs to stories. However,
evaluating objectively the creativity of a piece of writing is challenging. Inspired by the Torrance Test of Creative Thinking (TTCT)
[61], which measures creativity as a process, we use the Consensual Assessment Technique [3] and propose Torrance Test of Creative
Writing (TTCW) to evaluate creativity as product. TTCW consists of 14 binary tests organized into the original dimensions of Fluency,
Flexibility, Originality, and Elaboration. We recruit 10 creative writers and implement a human assessment of 48 stories written either
by professional authors or LLMs using TTCW. Our analysis shows that LLM-generated stories pass 3-10X less TTCW tests than stories
written by professionals. In addition, we explore the use of LLMs as assessors to automate the TTCW evaluation, revealing that none
of the LLMs positively correlate with the expert assessments.