CAP: Controllable Alignment Prompting for Unlearning in LLMs
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Wang, Zhaokun Guo, Jinyu Pu, Jingwen Pu, Hongli Yang, Meng Chen, Xunlei Ou, Jie Li, Wenyi Luo, Guangchun Tian, Wenhong |
| author_facet | Wang, Zhaokun Guo, Jinyu Pu, Jingwen Pu, Hongli Yang, Meng Chen, Xunlei Ou, Jie Li, Wenyi Luo, Guangchun Tian, Wenhong |
| contents | Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance and ethical safety. However, existing parameter-modifying methods face fundamental limitations: high computational costs, uncontrollable forgetting boundaries, and strict dependency on model weight access. These constraints render them impractical for closed-source models, yet current non-invasive alternatives remain unsystematic and reliant on empirical experience. To address these challenges, we propose the Controllable Alignment Prompting for Unlearning (CAP) framework, an end-to-end prompt-driven unlearning paradigm. CAP decouples unlearning into a learnable prompt optimization process via reinforcement learning, where a prompt generator collaborates with the LLM to suppress target knowledge while preserving general capabilities selectively. This approach enables reversible knowledge restoration through prompt revocation. Extensive experiments demonstrate that CAP achieves precise, controllable unlearning without updating model parameters, establishing a dynamic alignment mechanism that overcomes the transferability limitations of prior methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21251 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CAP: Controllable Alignment Prompting for Unlearning in LLMs Wang, Zhaokun Guo, Jinyu Pu, Jingwen Pu, Hongli Yang, Meng Chen, Xunlei Ou, Jie Li, Wenyi Luo, Guangchun Tian, Wenhong Machine Learning Artificial Intelligence Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance and ethical safety. However, existing parameter-modifying methods face fundamental limitations: high computational costs, uncontrollable forgetting boundaries, and strict dependency on model weight access. These constraints render them impractical for closed-source models, yet current non-invasive alternatives remain unsystematic and reliant on empirical experience. To address these challenges, we propose the Controllable Alignment Prompting for Unlearning (CAP) framework, an end-to-end prompt-driven unlearning paradigm. CAP decouples unlearning into a learnable prompt optimization process via reinforcement learning, where a prompt generator collaborates with the LLM to suppress target knowledge while preserving general capabilities selectively. This approach enables reversible knowledge restoration through prompt revocation. Extensive experiments demonstrate that CAP achieves precise, controllable unlearning without updating model parameters, establishing a dynamic alignment mechanism that overcomes the transferability limitations of prior methods. |
| title | CAP: Controllable Alignment Prompting for Unlearning in LLMs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.21251 |