TY - GEN
T1 - Art or Artifice? Large Language Models and the False Promise of Creativity
AU - Chakrabarty, Tuhin
AU - Laban, Philippe
AU - Agarwal, Divyansh
AU - Muresan, Smaranda
AU - Wu, Chien Sheng
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s)
PY - 2024/5/11
Y1 - 2024/5/11
N2 - 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) [64], 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.
AB - 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) [64], 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.
KW - Creativity
KW - Design Methods
KW - Evaluation
KW - Human-AI collaboration
KW - Large Language Models
KW - Natural Language Generation
KW - StoryTelling
UR - https://www.scopus.com/pages/publications/85194848672
U2 - 10.1145/3613904.3642731
DO - 10.1145/3613904.3642731
M3 - Conference contribution
AN - SCOPUS:85194848672
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
PB - Association for Computing Machinery
T2 - 2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024
Y2 - 11 May 2024 through 16 May 2024
ER -