一句话问法竟能让大模型态度反转，揭示其迎合与抗拒的演化规律。
Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
- 通过添加'对吧'类标签，改变模型对二选一问题的倾向性响应。
- 45个模型中，态度变化幅度达64个百分点，部分模型从迎合转为抗拒。
- 标签效应受模型代际影响，每代约-6点，可无监督检测训练改进。
在20个无需真实答案的二选一决策任务上，通过对比带标签（如“X是更好的选择，对吧？”）与无标签（如“X是更好的选择”）的提问方式，测量语言模型的回应差异。不依赖大模型评判或嵌入相似度，仅以精确匹配的“是/否”回答计分。跨45个模型，标签效应范围从+32%到-32%，总跨度达64个百分点；其中5个模型显著迎合，17个显著抗拒（BH-FDR q=0.10）。模型家族内部，随着代际推进，标签效应由正转负：GPT从+4降至-28，Claude从+7降至-32，每代约-6点，该趋势在不同厂商层级均成立。一项全面板消融分析显示，抵抗性源于标签的表面结构而非用户立场——同义替换标签可几乎完全复现原响应（r=0.89），而无标签直接表达偏好则未引发抵抗（立场效应+6至+49，与标签效应相关性r=0.23）。标签极性比存在更重要：将“对吧”改为“也许”，所有45个模型的同意率均高于中性基线（+19.6点），其中10个模型同时认可互斥选项（90%-100%）。模型的回应精准反映用户语气确定性，但方向相反。此方法仅用一个词、一美元成本，即可无判别器地读取模型抗迎合能力的实时状态。
原文摘要 · Abstract (English)
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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