PCA-guided Activation Scaling for Monotonic Bidirectional Control over LLM Sycophancy

Abstract:Large language models (LLMs) exhibit sycophancy, a tendency to agree with user beliefs regardless of factual accuracy. This can reinforce misconceptions, but eliminating it entirely risks over-correction against valid opinions. Effective control must therefore both reduce and increase sycophancy with predictable and gradual effect. Yet, existing methods fail to ensure a bidirectional and monotonic relationship between steering strength and behavioral outcome across models and datasets. We introduce PCA-guided Activation Scaling (PAS), an activation steering framework that decomposes residual stream activations into a PCA-identified sycophancy-honesty subspace and an orthogonal residual, then applies distinct scaling exponents to achieve monotonic, bidirectional control. Across three LLMs and three datasets, PAS achieves strong monotonicity (Spearman $\rho$ = +0.92) and an average shift of 15.4% per direction, compared with 8.7% for the baselines. Ablation studies confirm that the decomposition, asymmetric exponents, and layer selection are each essential for maintaining monotonic control. The data and code are available at this https URL.

Computation and Language (cs.CL)

arXiv:2608.16650 [cs.CL]

(or arXiv:2608.16650v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.16650 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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