title: Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering
    Target Atoms
summary: 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.
paper_authors._id: 68366ec11ec776c1b00a2ce3
paper_authors.name: Mengru Wang
paper_authors._id: 68366ec11ec776c1b00a2ce4
paper_authors.name: Ziwen Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:41:42.278Z
paper_authors._id: 6549caee44e75a7de4fee2fa
paper_authors.avatarUrl: /avatars/5aea69671eb1299aaaa948d888b4b64f.svg
paper_authors.fullname: Xu Ziwen
paper_authors.type: user
paper_authors.user: xzwnlp
paper_authors._id: 68366ec11ec776c1b00a2ce5
paper_authors.name: Shengyu Mao
paper_authors._id: 68366ec11ec776c1b00a2ce6
paper_authors.name: Shumin Deng
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/d3c4b759e6a5635e37ff715fae52e5ba.svg
paper_authors.fullname: Shumin Deng
paper_authors.type: user
paper_authors.user: 231sm
paper_authors._id: 68366ec11ec776c1b00a2ce7
paper_authors.name: Zhaopeng Tu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:42:08.529Z
paper_authors._id: 67485743561b1e6f9579389f
paper_authors.avatarUrl: /avatars/8a4cc63bd7be388010bc329bb74582a1.svg
paper_authors.fullname: Zhaopeng Tu
paper_authors.type: user
paper_authors.user: zptu
paper_authors._id: 68366ec11ec776c1b00a2ce8
paper_authors.name: Huajun Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:42:21.253Z
paper_authors._id: 67c06592ebc2e682b2628bde
paper_authors.avatarUrl: /avatars/1032c0319eded956d2dd461b0e1b4ddb.svg
paper_authors.fullname: Huajun Chen
paper_authors.type: user
paper_authors.user: HuajunChen
paper_authors._id: 68366ec11ec776c1b00a2ce9
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:14.445Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_title: Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering
    Target Atoms
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: sparse autoencoders disentangled knowledge components Steering Target Atoms robustness flexibility adversarial scenarios