Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering
  Target Atoms
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.
68366ec11ec776c1b00a2ce3
Mengru Wang
68366ec11ec776c1b00a2ce4
Ziwen Xu
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2025-05-28T12:41:42.278Z
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Xu Ziwen
user
xzwnlp
68366ec11ec776c1b00a2ce5
Shengyu Mao
68366ec11ec776c1b00a2ce6
Shumin Deng
admin_assigned
2025-05-28T12:42:03.038Z
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Shumin Deng
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Zhaopeng Tu
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2025-05-28T12:42:08.529Z
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Zhaopeng Tu
user
zptu
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Huajun Chen
admin_assigned
2025-05-28T12:42:21.253Z
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Huajun Chen
user
HuajunChen
68366ec11ec776c1b00a2ce9
Ningyu Zhang
claimed_verified
2025-05-28T08:58:14.445Z
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Ningyu Zhang
user
Ningyu
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering
  Target Atoms
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.
A novel method called Steering Target Atoms isolates and manipulates disentangled knowledge components in language models to improve safety, robustness, and flexibility, especially in adversarial scenarios.
sparse autoencoders disentangled knowledge components Steering Target Atoms robustness flexibility adversarial scenarios