A Unified Understanding and Evaluation of Steering Methods

Understand

Latent space steering methods provide a practical approach to controlling large language models by applying steering vectors to intermediate activations, guiding outputs toward desired behaviors while avoiding retraining.

• Despite their growing importance, the field lacks a unified understanding and consistent evaluation across tasks and datasets, hindering progress.
• This paper introduces a unified framework for analyzing and evaluating steering methods, formalizing their core principles and offering theoretical insights into their effectiveness.
• Through comprehensive empirical evaluations on multiple-choice and open-ended text generation tasks, we validate these insights, identifying key factors that influence performance and demonstrating the superiority of certain methods.