LLM写故事时比人类更确定，反而让作品缺乏创意。
LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writers
- 用信息论量化人类与模型在创作中的不确定性差异
- 28个LLM在优质故事数据集上均显示模型输出更确定
- 需新对齐范式保留有益模糊性，提升文学创造力
我们指出，不确定性是限制大语言模型在创造性写作中表现的关键因素，常导致内容陈词滥调。文学理论认为不确定性是创造性表达的必要条件，而当前对齐策略倾向于规避不确定输出以保证事实性和减少幻觉。本文通过在高质量叙事数据集上对28个LLM进行受控的信息论分析，量化了人类写作与模型续写之间的“不确定性差距”。结果表明，人类写作的不确定性显著高于模型输出；指令微调和推理模型比基础模型更加剧这一趋势；该差距在创造性写作领域比功能型任务更为明显，且与写作质量强相关。实现人类水平的创造力需要具备不确定性感知的新对齐范式，以区分有害幻觉与文学丰富性所需的建设性模糊。
原文摘要 · Abstract (English)
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.
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