Improving Activation Steering in Language Models with Mean-Centring
Ole Jorgensen Dylan Cope Nandi Schoots Murray Shanahan
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.