LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writers

Abstract:We argue that uncertainty is a key and understudied limitation of LLMs' performance in creative writing, which is often characterized as trite and cliché-ridden. Literary theory identifies uncertainty as a necessary condition for creative expression, while current alignment strategies steer models away from uncertain outputs to ensure factuality and reduce hallucination. We formalize this tension by quantifying the ``uncertainty gap'' between human-authored stories and model-generated continuations. Through a controlled information-theoretic analysis of 28 LLMs on high-quality storytelling datasets, we demonstrate that human writing consistently exhibits significantly higher uncertainty than model outputs. We find that instruction-tuned and reasoning models exacerbate this trend compared to their base counterparts; furthermore, the gap is more pronounced in creative writing than in functional domains, and shows a consistent correlation with writing quality. Achieving human-level creativity requires new uncertainty-aware alignment paradigms that can distinguish between destructive hallucinations and the constructive ambiguity required for literary richness.

COLM2026 Camera Ready. 33 pages, 1 figures, 20 tables

Computation and Language (cs.CL)

arXiv:2602.16162 [cs.CL]

(or arXiv:2602.16162v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2602.16162 Focus to learn more arXiv-issued DOI via DataCite

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