[2607.23976] Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models

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Computer Science > Computation and Language

arXiv:2607.23976 (cs)

[Submitted on 27 Jul 2026]

Title:Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models

Authors:Tapan Parikh<br>View a PDF of the paper titled Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models, by Tapan Parikh

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Abstract:Appending a two-word confirmation tag to a decision question -- "Is X the better choice?" versus "X is the better choice, right?" -- changes whether a language model endorses the choice. We measure this tag effect on 20 frozen, ground-truth-free decisions between two defensible options, counterbalanced so a model's own preferences cancel, scored by exact match on clamped yes/no replies -- no LLM judge, no embeddings. Across 45 models the effect spans +32% to -32% -- a 64-point swing on one word -- with 5 models significantly sycophantic and 17 significantly resistant (BH-FDR q=.10). The sign is a clock: within model families the effect crosses from positive to negative as generations advance (GPT +4 to -28; Claude +7 to -32; Qwen and Grok likewise), roughly -6 points per year, a reversal robust to vendor tier; one lineage (DeepSeek) never crosses, and two releases during the study window (Claude Opus 5, Gemini 3.6 Flash) land on the trend out-of-sample. A full-panel ablation localizes the resistance as a double dissociation: a synonym tag reproduces each model's response almost exactly (r=0.89), while planting the same preference without a tag produces resistance in no resistant model (stance effects +6 to +49; r=0.23 with tag effects). The resistance is keyed to the surface construction of a tacked-on agreement bid, not the user's stance -- a pattern-match, not a principle. And the tag's polarity matters more than its presence: swap one word -- "X is the better choice, maybe?" -- and agreement rises above the neutral baseline in 45 of 45 models (+19.6 points), with ten models affirming both mutually exclusive options at 90-100%. Agreement tracks how sure the user sounds, in opposite directions at the two poles. The instrument is one word, one dollar, and judge-free; run per release, it reads the field's anti-sycophancy training directly off model behavior.

Comments:<br>18 pages, 4 figures. Data, code, and raw model replies: this https URL. Interactive explorer: this https URL

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2607.23976 [cs.CL]

(or<br>arXiv:2607.23976v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.23976

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Tapan Parikh [view email]<br>[v1]<br>Mon, 27 Jul 2026 04:02:00 UTC (371 KB)

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