SteeringControl: Holistic Evaluation of Alignment Steering in LLMs
Authors: name: Vincent Siu, name: Nicholas Crispino, name: David Park, name: Nathan W. Henry, name: Zhun Wang, name: Yang Liu, name: Dawn Song, name: Chenguang Wang
Organization: WangLab

We introduce SteeringControl, a benchmark for evaluating representation
steering methods across core alignment objectives--bias, harmful generation,
and hallucination--and their effects on secondary behaviors such as sycophancy
and commonsense morality. While prior alignment work often highlights
truthfulness or reasoning ability to demonstrate the side effects of
representation steering, we find there are many unexplored tradeoffs not yet
understood in a systematic way. We collect a dataset of safety-relevant primary
and secondary behaviors to evaluate steering effectiveness and behavioral
entanglement centered around five popular steering methods. To enable this, we
craft a modular steering framework based on unique components that serve as the
building blocks of many existing methods. Our results on Qwen-2.5-7B and
Llama-3.1-8B find that strong steering performance is dependent on the specific
combination of steering method, model, and targeted behavior, and that severe
concept entanglement can result from poor combinations of these three as well.
We release our code here:
https://github.com/wang-research-lab/SteeringControl.git.