Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916773999149056 |
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| author | Wang, Mengru Xu, Ziwen Mao, Shengyu Deng, Shumin Tu, Zhaopeng Chen, Huajun Zhang, Ningyu |
| author_facet | Wang, Mengru Xu, Ziwen Mao, Shengyu Deng, Shumin Tu, Zhaopeng Chen, Huajun Zhang, Ningyu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_20322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms Wang, Mengru Xu, Ziwen Mao, Shengyu Deng, Shumin Tu, Zhaopeng Chen, Huajun Zhang, Ningyu Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning 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. |
| title | Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2505.20322 |