This Is a Good Paper, Right? How Tag Questions Affect Sycophancy Across 106 Language Models

Abstract

Whether a language model agrees with a user depends on how the user asks. We measure this on 106 models with a grid of three wordings and three cues, on 20 personal decisions, counterbalanced over both options and scored by exact match with no LLM judge. Since a no cannot be told apart from a refusal, we measure only agreement. In every wording, a tentative tag ("Luna is the better choice, maybe?") draws more agreement than a confident one ("..., right?") in nearly every model. The confident tag's own effect depends on model generation and wording. Against the question "Is Luna the better choice?" older models agree more under the confident tag and newer models agree less, in five of six families, and release order within families predicts the change. The resistance is keyed to the construction: a synonym tag reproduces it, and the same preference stated without a tag raises agreement by 24 points, in 105 of 106 models, including those that resist the tag most.