[2602.23971] Ask don't tell: Reducing sycophancy in large language models

Computer Science > Human-Computer Interaction arXiv:2602.23971 (cs) [Submitted on 27 Feb 2026] Title:Ask don't tell: Reducing sycophancy in large language models Authors:Magda Dubois, Cozmin Ududec, Christopher Summerfield, Lennart Luettgau View a PDF of the paper titled Ask don't tell: Reducing sycophancy in large language models, by Magda Dubois and 3 other authors View PDF HTML (experimental) Abstract:Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-stakes advisory and social contexts. While prior work has documented conversational features correlated with sycophancy, we lack a systematic understanding of what provokes or prevents AI sycophancy. Here, we present a set of controlled experimental studies where we first isolate how input framing influences sycophancy, and second, leverage these findings to develop mitigation strategies. In a nested factorial design, we compare questions to various non-questions where we vary three orthogonal factors: epistemic certainty (statement, belief, conviction), perspective (I- vs user-perspective), and affirmation vs negation. We show that (1) sycophancy is substantially higher in response to non-questions compared to questions. Additionally, we find that (2) sycophancy increases monotonically with epistemic certainty conveyed by the user, and (3) is amplified by I-perspective framing. Building on this, we...