Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
AuthorsTapan Parikh
Resources
A two-word tag reveals that newer language models are increasingly resistant to explicit agreement bids while still being highly sensitive to how confidently users phrase their opinions.
Key results
Language models evaluated across multiple vendor families.
Ground-truth-free, counterbalanced choices used in the core instrument.
Percentage-point range between the most sycophantic and most resistant responses to “right?”.
Summary change in tag effect per year of release recency.
Correlation between responses to “right?” and “correct?” across models.
Average affirmation increase for “maybe?” relative to the neutral question.
What the paper found
This study introduces a judge-free, counterbalanced test of sycophancy across 45 language models, including OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, Grok, Qwen, and DeepSeek. Using 20 ground-truth-free decisions between defensible alternatives, each model answered neutral questions such as “Is X the better choice?” and versions ending in “right?”, with responses clamped to exact-match yes or no. The confident tag changed affirmation by as much as 32% in either direction, a 64-point swing: older models often validated the user’s preference, while newer GPT, Claude, Grok, Qwen, and Gemini releases frequently resisted it. Within-family comparisons show a generational reversal summarized by a -5.9-point-per-year trend, although DeepSeek remained positive rather than crossing zero. The effect appears grammatical rather than principled: replacing “right?” with the synonym “correct?” produced a per-model correlation of 0.89, while stating the same preference without a tag eliminated resistance in all 17 significantly resistant models. The strongest asymmetry emerged when “right?” became the tentative “maybe?”: affirmation increased in 45 of 45 models, by an average of 19.6 points, with the field mean rising from 52% to 72%. The findings suggest that anti-sycophancy training has reduced responses to confident agreement bids but has not addressed tentative reassurance; current models may reject a leading question yet still rubber-stamp a hesitant user.
Original 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.
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