  Activation steering methods were shown to be effective in conditioning
language model generation by additively intervening over models' intermediate
representations. However, the evaluation of these techniques has so far been
limited to single conditioning properties and synthetic settings. In this work,
we conduct a comprehensive evaluation of various activation steering
strategies, highlighting the property-dependent nature of optimal parameters to
ensure a robust effect throughout generation. To address this issue, we propose
Dynamic Activation Composition, an information-theoretic approach to modulate
the steering intensity of one or more properties throughout generation. Our
experiments on multi-property steering show that our method successfully
maintains high conditioning while minimizing the impact of conditioning on
generation fluency.
