Humans Introduce, Models Elaborate: Asymmetric Narrative Agency in Human-LLM Co-Writing

2026-09-07

AI Summary

研究通过比较人类-人类、人类-LLM和LLM-LLM三种合作模式下的叙事互动，发现人类倾向于引入新颖持久的材料，而LLM则更擅长扩展和稳定现有内容。

Abstract

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.

Problem

• Human-LLM co-writing
• narrative development
• collaborative storytelling

Innovation

• Human-LLM Co-Writing
• Asymmetric Narrative Agency
• Semantic Novelty
• Narrative Amplifiers