What Can We Actually Steer? A Multi-Behavior Study of Activation Control
Tetiana Bas, Krystian Novak

TL;DR
This study empirically analyzes how activation steering effectiveness in large language models varies across different behavior types, revealing that behavior nature significantly influences steering success and providing practical guidance for implementation.
Contribution
It offers a comprehensive empirical analysis of activation steering across diverse behaviors, highlighting the impact of behavior type and data size on steering effectiveness.
Findings
Steering effectiveness varies significantly by behavior type.
Trait expression follows an inverted-U response to steering strength.
Larger datasets enable more aggressive and effective steering.
Abstract
Large language models (LLMs) require precise behavior control for safe and effective deployment across diverse applications. Activation steering offers a promising approach for LLMs' behavioral control. We focus on the question of how steering effectiveness varies across different behavior types and whether the nature of target behaviors can predict steering success. We address this through empirical analysis of activation steering across 50 behaviors that span persona archetypes, personality traits, misalignment behaviors, style cues, and impersonation of public figures. We present a set of comprehensive experiments on coefficient optimization, vector properties, and data requirements to provide comprehensive guidance for the implementation of activation steering. Our analysis demonstrates that steering effectiveness varies significantly by behavior type, with different behavioral…
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Taxonomy
TopicsTopic Modeling · Ethics and Social Impacts of AI · Mental Health via Writing
