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 strongly correlates to 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.

翻译：我们认为，不确定性是限制大型语言模型在创意写作中表现的一个关键且未被充分研究的因素，此类写作常被描述为陈腐且充满陈词滥调。文学理论将不确定性视为创造性表达的必要条件，而当前的模型对齐策略却引导模型远离不确定的输出，以确保事实准确性并减少幻觉。我们通过量化人类创作故事与模型生成续写之间的“不确定性差距”来形式化这一矛盾。通过对28个大型语言模型在高质量叙事数据集上进行受控的信息论分析，我们证明人类写作始终表现出比模型输出显著更高的不确定性。我们发现，相较于基础模型，经过指令微调和推理训练的模型加剧了这一趋势；此外，这种差距在创意写作领域比在功能性领域更为明显，并且与写作质量高度相关。要达到人类水平的创造力，需要建立能够区分破坏性幻觉与文学丰富性所需之建设性模糊的新型不确定性感知对齐范式。