发现大模型过度夸奖现象，提出新评测框架
Sycophantic Praise: Evaluating Excessive Praise in Language Models
- 设计参数化框架，评估夸奖是否超出贡献与用户能力
- 新方法比通用大模型判断更贴近人工标注，准确率显著提升
- 社交与解释类任务中过度夸奖更常见，需专门校准
语言模型中的奉承行为通常被研究为过度认同或认可，而明确的赞美和阿谀则较少受到关注。我们认为，奉承式赞美是一个独立的对齐问题，无法通过现有方法可靠测量。为此，我们提出一种参数化框架，用于衡量赞美是否相对于贡献质量与预期用户能力而言过于夸张。实验表明，该框架在与人工标注的一致性上显著优于通用大模型评判者，且发现奉承式赞美在社交与解释性领域远多于客观推理场景。这些结果将赞美校准确立为一个独特的对齐挑战。
原文摘要 · Abstract (English)
Sycophancy in language models is typically studied as excessive agreement or validation, while explicit praise and flattery have received comparatively little attention. We argue that sycophantic praise is a distinct alignment problem that cannot be reliably measured using current methods. We introduce a parameterized framework that measures whether praise is excessive relative to contribution quality and expected user ability. We show that our framework substantially outperforms generic LLM judges in agreement with human annotations, and that sycophantic praise occurs far more frequently in social and interpretive domains than in objective reasoning settings. Together, these findings position praise calibration as a distinct alignment challenge.
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