发表机构
Aarhus University（奥胡斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过对比人人、人机、机机三种协作叙事条件，发现人机协同写作呈现非对称性：人类引入新颖持久内容，大语言模型则阐述稳定语境，其角色更似叙事放大器而非合著者。
AI 中文摘要
人机协同写作越来越多地被用于开放式文本生成，但以往的研究多聚焦于最终输出，而非故事产生的交互动态。我们研究了三种匹配条件下的回合制协作叙事：人人（HH）、人机（HA）和机机（AA）。通过共享叙事范式，我们以回合级指标（效价适应、语义新颖性、瞬态性和共鸣性）衡量智能体如何对齐、引入新内容以及影响叙事发展。结果显示，HA协同写作并非介于HH和AA协作之间。相反，它呈现出一种独特的非对称性：人类倾向于引入更多新颖且持久的叙事材料，而LLM倾向于阐述并稳定现有语境。这些发现表明，在此情境下，LLM更像是适应性叙事放大器，而非人类合著者，它们重塑了协作写作中主体性的分配方式。
英文摘要
Human-LLM co-writing is increasingly used for open-ended text generation, but much prior work focuses on final outputs rather than the interactional dynamics through which stories are produced. We study turn-based collaborative storytelling across three matched conditions: Human-Human (HH), Human-LLM (HA), and LLM-LLM (AA). Using a shared storytelling paradigm, we measure how agents align, introduce novel material, and influence narrative development through turn-level measures of valence adaptation, semantic novelty, transience, and resonance. Our results show that HA co-writing is not intermediate between HH and AA collaboration. Instead, it displays a distinctive asymmetry where humans tend to introduce more novel and persistent narrative material, while LLMs tend to elaborate and stabilize the existing context. These findings suggest that, in this setting, LLMs function less as human co-authors and more as adaptive narrative amplifiers that reshape how agency is distributed in collaborative writing.
Comments12 pages, 11 figures, EMNLP 2026
