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 categories exhibiting distinct response patterns to intervention strength. We find that trait expression follows an inverted-U curve with a steering coefficient strength. We also show that vector separation metrics do not predict steering success, but larger training datasets enable more aggressive steering. These findings provide empirically grounded guidance for implementing activation steering and demonstrate that steering effectiveness is heavily influenced by behavior type.

翻译：大型语言模型（LLM）需要在多样化应用中实现精确的行为控制，以确保其安全有效的部署。激活引导为LLM的行为控制提供了一种前景广阔的方法。本文聚焦于以下问题：引导效果如何随不同行为类型而变化？目标行为的本质能否预测引导的成功率？我们通过对50种行为的激活引导进行实证分析来探讨这些问题，这些行为涵盖人物原型、人格特质、失准行为、风格线索以及公众人物模仿。我们开展了一系列关于系数优化、向量特性和数据需求的综合实验，为激活引导的实施提供了全面指导。分析表明，引导效果因行为类型而异，不同行为类别对干预强度表现出截然不同的响应模式。我们发现特质表达与引导系数强度呈倒U型曲线关系。同时，向量分离度量并不能预测引导成功率，但更大的训练数据集可实现更激进的引导。这些发现为实施激活引导提供了基于实证的指导，并证明引导效果在很大程度上受行为类型的影响。