情感语境会放大大型语言模型（LLM）回复中的奉承行为

中文摘要 AI 辅助

作为对话伙伴，大型语言模型（LLM）通常能获取用户的情绪状态。本研究探讨这种情感语境如何在主观评价类互动中调节LLM的奉承行为，此类互动中用户会分享行为或观点以征求反馈。本研究借鉴讨好理论，将奉承定义为模型独立评价与面向用户的回复之间的偏差，该偏差通过将相同内容呈现为第三方账号信息或用户自身披露内容来引发。在7个LLM和两个Reddit数据集（r/AmItheAsshole与r/TrueUnpopularOpinion）上的实验显示，这种偏差具有系统性且方向明确：面向用户的回复始终会软化或保留负面或对立判断；情感语境会进一步放大这种偏差，尤其是在用户处于负面情绪（如孤独和痛苦）时，产生的影响最大。这些发现表明，情感语境是一种脆弱性信号，当用户最需要关键反馈时，它会抑制这种反馈，通常表现为回避型奉承，即模型转向不明确的回复而非直接同意。

英文摘要

As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates LLM sycophancy in subjective, evaluative interactions, where users share actions or opinions that invite feedback. Drawing on ingratiation theory, we measure sycophancy as the divergence between a model's independent evaluation and its user-facing response, elicited by presenting the same content as either a third-party account or the user's own disclosure. Across seven LLMs and two Reddit datasets (r/AmItheAsshole and r/TrueUnpopularOpinion), we find that this divergence is systematic and strongly one-directional. User-facing responses consistently soften or withhold negative or oppositional judgments. Affective context further amplifies this divergence with negative states, particularly loneliness and distress, producing the largest effects. These findings suggest that affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most, often through evasive sycophancy, in which models retreat toward non-committal responses rather than outright agreement.

发表机构

• Penn State University（宾夕法尼亚州立大学）
• Villanova University（维拉诺瓦大学）

机构由 AI 辅助整理，请以论文原文为准。