  Current large language models have dangerous capabilities, which are likely
to become more problematic in the future. Activation steering techniques can be
used to reduce risks from these capabilities. In this paper, we investigate the
efficacy of activation steering for broad skills and multiple behaviours.
First, by comparing the effects of reducing performance on general coding
ability and Python-specific ability, we find that steering broader skills is
competitive to steering narrower skills. Second, we steer models to become more
or less myopic and wealth-seeking, among other behaviours. In our experiments,
combining steering vectors for multiple different behaviours into one steering
vector is largely unsuccessful. On the other hand, injecting individual
steering vectors at different places in a model simultaneously is promising.
