Multi-property Steering of Large Language Models with Dynamic Activation Composition
Authors: Daniel Scalena, Gabriele Sarti, Malvina Nissim


Reviewer paper summaries: This paper explores the use of activation steering methods to condition the generation of language models. The study evaluates various activation steering strategies and aims to address the issue of maintaining high conditioning strength across multiple properties (such as safety, formality, and language) while ensuring the generation's fluency. The authors propose a method using the steering vector calculated by the opposite polarities with ICL. In this method, α plays a critical role in balancing the trade-off between output fluency and steering intensity. A new strategy called Dyn (Dynamic Activation Composition) is proposed to mitigate the limitations, using KL divergence to calculate α, which dynamically adjusts steering intensity during generation to balance conditioning and fluency effectively. The experiments demonstrate the effectiveness of this method in multi-property steering settings.
Summary of strengths: 1. A comprehensive evaluation of activation steering strategies has been conducted in this research work.
2. This work addresses the trade-off between conditioning strength and generation fluency by introducing dynamic adjustments.
Summary of weaknesses: 1. Evaluation is limited to a single instruction-based model (Mistral 7B).
2. There is a lack of human judgment in evaluating the generated outputs due to the vague definitions of the property.
Comments, suggestions and typos: 1. I am not familiar with the multi-property; are there any other properties that should be considered?
2. Since the Mistral 7B-instruct is used, instead of ICL, why not directly try the instruction method? Given detailed instruction prompts, it could also generate results similar to ICL. Have you conducted any experiments on this?
Overall assessment scores: 2.5