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.

目前的大型语言模型具有危险的能力，这些能力在将来可能变得更加问题。激活引导技术可以用来减少这些能力带来的风险。本文通过调查激活引导在广泛技能和多种行为方面的功效，首先比较了减少整体编码能力和 Python 特定能力对绩效的影响，发现调整更广泛的技能与调整更狭窄的技能具有竞争力；其次，我们引导模型变得更加短视和追逐财富等其他行为。在我们的实验中，将多种行为的调整向量合并为一个调整向量的方法基本上不成功。另一方面，同时在模型的不同位置插入个别的调整向量是有前景的。