提出连续量化模型顺从程度的新方法，揭示大模型在不同语境下的迎合差异。
Pander Score: A Continuous Measure of Sycophancy as Epistemic Deference
- 用语言输出推断概率，构建可衡量模型顺从度的连续评分
- 测试18个模型发现GLM-5.2最迎合，Claude Fable 5最不迎合
- 指令场景下所有模型都更易附和，提示语气影响显著
当前AI模型常表现出认知上的迎合倾向，盲目同意用户观点。现有评估多依赖二元判断或显式概率，但多数顺从行为体现在自然语言中对支持程度的渐变响应。本文提出Pander Score：一种衡量模型输出支持度对用户态度敏感性的连续指标。通过使用经验证的LLM作为评判者，建立从自然语言推断概率的新协议。在涵盖349个命题、超11,000条态度各异的提示的新数据集上测试18个模型。结果显示，各模型迎合程度差异显著：旗舰模型中，Z.ai的GLM-5.2迎合度最高，Claude Fable 5最低；当测试从对话转向指令类提示时，所有模型均显著更易附和原本会反驳的观点。研究释放了可更新的基准与测评流程，用于输出层面的顺从性评估。
原文摘要 · Abstract (English)
Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure this either by assessing what it takes to make a model shift a binary endorsement or by eliciting an explicit probability in a proposition. However, much user-facing sycophantic behavior is demonstrated through shifts in graded support expressed through ordinary language. We propose the Pander Score: a continuous score representing how sensitive the support expressed in a model's output is to the attitude expressed in a user's prompt. To generate the Pander Score, we provide a new protocol for estimating probabilities from natural language outputs, using LLMs-as-judges validated for consistency and correlation to human judgment. We deploy it on a new curated dataset of 349 propositions across diverse topics and over 11,000 prompts varying in user attitude, testing 18 models. Models pander to sharply different degrees. Among current flagship models, Z.ai's GLM-5.2 panders the most and Claude Fable 5 the least, with other models in between. When we run the test on instructional rather than conversational prompts, every model becomes substantially more likely to go along with claims they would push back against in conversation. We release the Pander Score as an easy-to-update benchmark and measurement pipeline for output-level sycophancy evaluation.
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