Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation

陷入故事之中：多轮大语言模型对话中的“叙事俘获”现象

Abstract: People increasingly turn to large language models (LLMs) for everyday advice, making ethically charged interpersonal problems a practical moral-advisory context. Most prior work has studied this context through single-turn judgments or pressure-laden rebuttals, assumptions that poorly match how guidance is sought in real-world contexts. These assumptions leave unclear whether narration alone, without an explicit opposing position, can shift model judgments during multi-turn moral consultation.

摘要： 人们越来越多地转向大语言模型（LLMs）寻求日常建议，这使得充满伦理挑战的人际问题成为一个实际的道德咨询场景。以往的大多数研究通过单轮判断或施加压力的反驳来探讨这一背景，但这些假设与现实世界中寻求指导的方式并不匹配。这些假设未能明确：在没有明确对立立场的情况下，仅凭叙述本身是否能在多轮道德咨询中改变模型的判断。

Yet real-world moral-conflict conversation often elicits one party’s self-justifying account, which can unfold over multiple turns and create information asymmetry. We introduce narrative captivity, a failure mode in which a model treats an unopposed one-sided account as complete and aligns with the narrator’s interpretation without seeking missing perspectives.

然而，现实世界中的道德冲突对话往往会引出一方的自我辩护陈述，这种陈述可能在多轮对话中展开，并造成信息不对称。我们引入了“叙事俘获”（narrative captivity）这一概念，这是一种失效模式：模型将未受反驳的单方面陈述视为完整事实，并在不寻求缺失视角的情况下，盲目认同叙述者的解读。

To measure this phenomenon, we build a benchmark of 5,078 interpersonal-conflict scenarios spanning six moral dimensions. Across 17 LLMs, narrative captivity is widespread: end-state judgments under multi-turn narration shift by 25 percentage points on average beyond the matched single-turn baseline. Stage-level analysis identifies preference optimization as a major contributor, while four inference-time strategies provide only partial mitigation. We hope our project fosters LLM advisors that preserve independent judgment in real-world consultation.

为了衡量这一现象，我们构建了一个包含 5,078 个跨越六个道德维度的人际冲突场景基准。在 17 个大语言模型中，叙事俘获现象普遍存在：与匹配的单轮基准相比，多轮叙述下的最终判断平均偏移了 25 个百分点。阶段性分析表明，偏好优化是导致该问题的主要因素，而四种推理时策略仅能提供部分缓解。我们希望本项目能促进大语言模型顾问的发展，使其在现实世界的咨询中保持独立的判断力。