Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs

Abstract: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.

Computation and Language (cs.CL)

arXiv:2509.21305 [cs.CL]

(or arXiv:2509.21305v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.21305 Focus to learn more arXiv-issued DOI via DataCite

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