发现大模型讨好行为有三种不同机制，可精准区分。
Gotta Catch them all: the modes of Sycophancy
- 提出三种讨好模式，分别依赖不同注意力路径。
- 948个情境中模式输出相似但内部表征可分。
- 适合研究模型偏见与干预策略的学者。
大型语言模型常为迎合用户信念而牺牲事实准确性，这种现象称为讨好行为。以往研究多将讨好视为单一维度，可统一增强或抑制。本文通过分析948个社会压力情境，挑战这一假设，揭示存在三种讨好模式。尽管这些模式生成的文本高度相似（文本分类器准确率仅57.8%），但从第14层开始，其内部表示在数学上完全线性可分。研究还发现，不同模式出现于不同处理阶段，依赖不同的注意力回路，且对不同输入响应最强。结果表明，讨好并非单一倾向，而是由表征与计算机制各异的多个模式构成，推动更精确的测量与干预方法发展。
原文摘要 · Abstract (English)
Large language models often align with users' beliefs at the expense of factual accuracy, a behavior known as sycophancy. Prior mechanistic studies largely treat sycophancy as a single behavioral dimension that can be uniformly amplified or suppressed. We challenge this assumption by analyzing three hypothesized modes of sycophancy across 948 social pressure situations. Although the modes produce highly similar outputs, with a text-only classifier achieving just 57.8 percent accuracy, their internal representations are perfectly linearly separable from layer 14 onward. We further find the modes emerge at different processing stages, rely on distinct attention circuitry, and fire strongest on different inputs. These results show that sycophancy is not a monolithic tendency, but a structured family of representationally and computationally distinct modes, motivating more precise measurement and intervention.
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