Applying steering vectors to large language models (LLMs) is an efficient and effective model alignment technique, but we lack an interpretable explanation for how it works-- specifically, what internal mechanisms steering vectors affect and how this results in different model outputs. To investigate the causal mechanisms underlying the effectiveness of steering vectors, we conduct a comprehensive case study on refusal. We propose a multi-token activation patching framework and discover that different steering methodologies leverage functionally interchangeable circuits when applied at the same layer. These circuits reveal that steering vectors primarily interact with the attention mechanism through the OV circuit while largely ignoring the QK circuit-- freezing all attention scores during steering drops performance by only 8.75% across two model families. A mathematical decomposition of the steered OV circuit further reveals semantically interpretable concepts, even in cases where the steering vector itself does not. Leveraging the activation patching results, we show that steering vectors can be sparsified by up to 90-99% while retaining most performance, and that different steering methodologies agree on a subset of important dimensions.

翻译：应用引导向量到大型语言模型（LLMs）是一种高效且有效的模型对齐技术，但我们缺乏对其工作机理的可解释性理解——具体而言，引导向量影响了哪些内部机制，以及这些影响如何导致不同的模型输出。为了探究引导向量有效性的因果机制，我们对“拒绝”行为进行了全面的案例研究。我们提出了一个多令牌激活补丁框架，并发现不同的引导方法在应用于相同层级时，能够利用功能可互换的电路。这些电路揭示出，引导向量主要通过OV电路与注意力机制交互，而基本上忽略了QK电路——在引导过程中冻结所有注意力分数仅使两个模型家族的性能下降8.75%。对经引导的OV电路进行数学分解进一步揭示了语义上可解释的概念，即使在引导向量本身不具备这种可解释性的情况下也是如此。利用激活补丁的结果，我们展示了引导向量可以被稀疏化高达90-99%而仍能保留大部分性能，并且不同的引导方法在一组重要维度上达成一致。