Recent work in activation steering has demonstrated the potential to better
control the outputs of Large Language Models (LLMs), but it involves finding
steering vectors. This is difficult because engineers do not typically know how
features are represented in these models. We seek to address this issue by
applying the idea of mean-centring to steering vectors. We find that taking the
average of activations associated with a target dataset, and then subtracting
the mean of all training activations, results in effective steering vectors. We
test this method on a variety of models on natural language tasks by steering
away from generating toxic text, and steering the completion of a story towards
a target genre. We also apply mean-centring to extract function vectors, more
effectively triggering the execution of a range of natural language tasks by a
significant margin (compared to previous baselines). This suggests that
mean-centring can be used to easily improve the effectiveness of activation
steering in a wide range of contexts.

通过应用均值中心化的思想，我们发现将与目标数据集相关的激活平均值减去所有训练激活的均值，可以得到有效的转向向量，这在广泛的上下文中可以轻松改善激活转向的有效性。