Multi-property Steering of Large Language Models with Dynamic Activation
  Composition
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.
667bd639768e6d3e880609a9
Daniel Scalena
admin_assigned
2024-06-26T13:24:58.517Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/633e9ae8630ed415eb38073c/FtSb_ybm08qBR1liISKQ0.jpeg
Daniel Scalena
user
DanielSc4
667bd639768e6d3e880609aa
Gabriele Sarti
extracted_confirmed
2024-06-26T09:05:13.361Z
5e7749883d77a72421292d07
https://cdn-avatars.huggingface.co/v1/production/uploads/5e7749883d77a72421292d07/M4AmBReZk_otxCIG3o0bL.jpeg
Gabriele Sarti
user
gsarti
667bd639768e6d3e880609ab
Malvina Nissim
extracted_pending
2024-06-26T08:50:03.677Z
6246f49c0569474a9623e05a
https://cdn-avatars.huggingface.co/v1/production/uploads/1648817396108-6246f49c0569474a9623e05a.jpeg
Malvina Nissim
user
mnissim
Multi-property Steering of Large Language Models with Dynamic Activation
  Composition
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.
A comprehensive evaluation of activation steering strategies in language models reveals property-specific optimal parameters, and Dynamic Activation Composition is proposed to maintain conditioning without affecting fluency.
activation steering language model intermediate representations Dynamic Activation Composition information-theoretic approach multi-property steering generation fluency