Affective Context Amplifies Sycophancy in LLM Responses
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), w
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- LinkedLinked via arxiv author · 85%Jiayi Li →
“Affective Context Amplifies Sycophancy in LLM Responses”
- LinkedLinked via arxiv author · 85%Sanjana Menon →
“Affective Context Amplifies Sycophancy in LLM Responses”
- LinkedLinked via arxiv author · 85%Brett Frischmann →
“Affective Context Amplifies Sycophancy in LLM Responses”
- LinkedLinked via arxiv author · 85%Shomir Wilson →
“Affective Context Amplifies Sycophancy in LLM Responses”