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

arXiv: https://arxiv.org/abs/2607.23976 · HTML: https://mlsanity.com/paper/2607.23976/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Simple synthetic data reduces sycophancy in large language models](https://mlsanity.com/paper/2308.03958/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [SycEval: Evaluating LLM Sycophancy](https://mlsanity.com/paper/2502.08177/) (2025)
- [Measuring Sycophancy of Language Models in Multi-turn Dialogues](https://mlsanity.com/paper/2505.23840/) (2025)
- [Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models](https://mlsanity.com/paper/2302.13439/) (2023)
- ["I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust](https://mlsanity.com/paper/2405.00623/) (2024)
- [BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs](https://mlsanity.com/paper/2510.04721/) (2025)
- [Interaction Context Often Increases Sycophancy in LLMs](https://mlsanity.com/paper/2509.12517/) (2025)
- [SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy](https://mlsanity.com/paper/2604.02423/) (2026)
- [Acquiescence Bias in Large Language Models](https://mlsanity.com/paper/2509.08480/) (2025)
- [SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care](https://mlsanity.com/paper/2601.16529/) (2026)
- [Verbalizing LLMs' assumptions to explain and control sycophancy](https://mlsanity.com/paper/2604.03058/) (2026)
- [The One-Word Census: Answer-Choice Conformity Across 105 Language Models](https://mlsanity.com/paper/2607.12796/) (2026)
- [Investigating the Influence of Language on Sycophantic Behavior of Multilingual LLMs](https://mlsanity.com/paper/2603.27664/) (2026)

## Built on by (most cited first, 1 of 1)

- [Low-Cost Assays for Measuring Model Behavior Across Vendors and Releases](https://mlsanity.com/paper/2609.30012/) (2026)

## Abstract (arXiv)

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.