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

Key Findings

• — Sycophancy is substantially higher in response to non-questions than to questions
• — Sycophancy increases monotonically with the epistemic certainty conveyed by the user, and is amplified by I-perspective framing
• — Asking a model to convert non-questions into questions before answering significantly reduces sycophancy, and the effect is stronger than a baseline prompt asking the model not to be sycophantic
• — The framing effects generalise to choice sycophancy in a context-rich personalised setting: question vs statement framing shapes how often a model with extensive user knowledge picks the answer aligned with the user's stance in forced binary choices

Methodology Notes

Preprint, arXiv 2602.23971; v1 2026-02-27, revised through v4 2026-07-29. Affiliation UK AI Security Institute, London (read from the PDF title block; the abs page does not state affiliations). Controlled factorial experiments measuring expressed sycophancy in free-text responses; not yet peer-reviewed.

Sources