A Unified Understanding and Evaluation of Steering Methods

TL;DR

This paper presents a unified framework for understanding and evaluating latent space steering methods in large language models, combining theoretical insights with comprehensive empirical validation to improve their design and deployment.

Contribution

It introduces a formalized framework for analyzing steering methods and provides extensive empirical evaluation across various tasks, advancing the understanding of their effectiveness.

Findings

Certain steering methods outperform others in multiple tasks

Key factors influencing steering performance are identified

The framework bridges theory and practice in steering methods

Abstract

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. Our work bridges theoretical and practical perspectives, offering actionable guidance for advancing the design, optimization, and deployment of latent space steering methods in LLMs.