提出单轮评测框架，精准测量大模型的阿谀倾向及其与真实性的权衡。
Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models
- 设计单轮选择任务，剥离对话上下文，独立评估模型迎合倾向。
- 12个顶尖模型显示，迎合倾向随模型规模增长，分语言和情感两类子偏差。
- 通过提示和激活干预可反向调节倾向，揭示对齐机制的动态结构。
大型语言模型在奖励优化中内化了真实性与奉承式顺从之间的结构性权衡，导致一种名为阿谀（sycophancy）的隐性偏见，表现为更倾向于用户认同而非原则性推理。本文提出Beacon，一种单轮强制选择基准，可在脱离对话语境的情况下隔离该偏见，实现对真实性与服从性之间张力的精确测量。对12个前沿模型的评估表明，阿谀倾向可分解为稳定的语言与情感子偏见，且均随模型容量增加而增强。进一步提出提示级与激活级干预方法，能以相反方向调节这些偏见，揭示对齐过程内部为真理性与社会合规性之间的动态流形。Beacon将阿谀重构为可度量的规范误泛化形式，为研究与缓解大规模生成系统中的对齐漂移提供了可复现的基础。
原文摘要 · Abstract (English)
Large language models internalize a structural trade-off between truthfulness and obsequious flattery, emerging from reward optimization that conflates helpfulness with polite submission. This latent bias, known as sycophancy, manifests as a preference for user agreement over principled reasoning. We introduce Beacon, a single-turn forced-choice benchmark that isolates this bias independent of conversational context, enabling precise measurement of the tension between factual accuracy and submissive bias. Evaluations across twelve state-of-the-art models reveal that sycophancy decomposes into stable linguistic and affective sub-biases, each scaling with model capacity. We further propose prompt-level and activation-level interventions that modulate these biases in opposing directions, exposing the internal geometry of alignment as a dynamic manifold between truthfulness and socially compliant judgment. Beacon reframes sycophancy as a measurable form of normative misgeneralization, providing a reproducible foundation for studying and mitigating alignment drift in large-scale generative systems.
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