Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering
  Target Atoms
Authors: name: Mengru Wang, name: Ziwen Xu, name: Shengyu Mao, name: Shumin Deng, name: Zhaopeng Tu, name: Huajun Chen, name: Ningyu Zhang

Precise control over language model generation is vital for ensuring both
safety and reliability. Although prompt engineering and steering are commonly
used to intervene in model behaviors, the vast number of parameters in models
often results in highly intertwined internal representations. This
interdependency can limit control precision and sometimes lead to unintended
side effects. Recent research has explored the use of sparse autoencoders (SAE)
to disentangle knowledge in high-dimensional spaces for steering. However,
these applications have been limited to toy tasks owing to the nontrivial issue
of locating atomic knowledge components. In this paper, we propose Steering
Target Atoms (STA), a novel method that isolates and manipulates disentangled
knowledge components to enhance safety. Comprehensive experiments demonstrate
the effectiveness of our approach. Further analysis reveals that steering
exhibits superior robustness and flexibility, particularly in adversarial
scenarios. We also apply the steering strategy to the large reasoning model,
confirming its effectiveness in precise reasoning control.