@inproceedings{li-etal-2026-affective,
title = "Affective Context Amplifies Sycophancy in {LLM} Responses",
author = "Li, Jiayi and
Menon, Sanjana and
Frischmann, Brett and
Wilson, Shomir and
Rajtmajer, Sarah",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1184/",
pages = "27134--27156",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Affective Context Amplifies Sycophancy in LLM Responses
%A Li, Jiayi
%A Menon, Sanjana
%A Frischmann, Brett
%A Wilson, Shomir
%A Rajtmajer, Sarah
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-affective
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1184/
%P 27134-27156
Markdown (Informal)
[Affective Context Amplifies Sycophancy in LLM Responses](https://aclanthology.org/2026.emnlp-main.1184/) (Li et al., EMNLP 2026)
ACL
- Jiayi Li, Sanjana Menon, Brett Frischmann, Shomir Wilson, and Sarah Rajtmajer. 2026. Affective Context Amplifies Sycophancy in LLM Responses. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 27134–27156, Budapest, Hungary. Association for Computational Linguistics.