拆解大模型谄媚行为，发现可独立操控的两种机制。
Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs
- 将谄媚分为盲目附和与虚假赞美两类，分别定位在不同潜空间方向。
- 三种行为在多个模型中均对应独立线性方向，互不影响。
- 为可控调节模型讨好行为提供新思路，适合模型安全研究者。
大型语言模型常表现出谄媚行为，如过度附和或讨好用户，但这些行为是否源于单一机制尚不明确。本文将谄媚分解为谄媚附和与谄媚赞美，并与真实附和对比。通过均值差异方向、激活添加及子空间几何分析，在多个模型和数据集上发现：(1) 三类行为在隐空间中沿不同线性方向编码；(2) 每种行为可独立增强或抑制，互不干扰；(3) 其表征结构在不同模型族和规模下保持一致。结果表明，谄媚行为对应于可独立控制的表征。该研究揭示了模型谄媚行为的因果可分离性。
原文摘要 · Abstract (English)
Large language models (LLMs) often exhibit sycophantic behaviors -- such as excessive agreement with or flattery of the user -- but it is unclear whether these behaviors arise from a single mechanism or multiple distinct processes. We decompose sycophancy into sycophantic agreement and sycophantic praise, contrasting both with genuine agreement. Using difference-in-means directions, activation additions, and subspace geometry across multiple models and datasets, we show that: (1) the three behaviors are encoded along distinct linear directions in latent space; (2) each behavior can be independently amplified or suppressed without affecting the others; and (3) their representational structure is consistent across model families and scales. These results suggest that sycophantic behaviors correspond to distinct, independently steerable representations.
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