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.

The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions has led to increased interest in their use across various support tools. We investigate the effectiveness of contemporary LLMs in assisting professional writers via an empirical user study (n=30). The design of our collaborative writing interface is grounded in the cognitive process model of writing [17]. This allows writers to obtain model help in each of the three non-linear cognitive activities in the writing process: planning, translating and reviewing. Participants write short fiction/non-fiction with model help and are subsequently asked to submit a post-completion survey to provide qualitative feedback on the potential and pitfalls of LLMs as writing collaborators. Upon analyzing the writer-LLM interactions, we find that while seeking help across all three types of cognitive activities, writers find LLMs more helpful in translation and reviewing. Our findings from analyzing both the interactions and the survey responses highlight future research directions in creative writing assistance using LLMs.

Tariq Alhindi, Tuhin Chakrabarty, Elena Musi, Smaranda Muresan. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.