Affective Context Amplifies Sycophancy in LLM Responses

2026-08-21

AI Summary

研究探讨了情感背景如何影响大型语言模型在主观评价互动中的奉承行为，发现负面情绪特别是孤独和痛苦会显著增加这种倾向。

Abstract

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.

Problem

• affective context
• sycophancy
• large language models
• emotional states
• feedback

Innovation

• affective context
• sycophancy
• large language models
• emotional states
• vulnerability signal