From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917419406065664 |
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| author | Xu, Xiaoyu Du, Minxin Li, Zitong Liang, Zi Guo, Zhibiao Zhang, Shiyu Hu, Peizhao Ye, Qingqing Hu, Haibo |
| author_facet | Xu, Xiaoyu Du, Minxin Li, Zitong Liang, Zi Guo, Zhibiao Zhang, Shiyu Hu, Peizhao Ye, Qingqing Hu, Haibo |
| contents | Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfully represent the true ``forgetting scope'' learned by the model. We formalize two distinct unlearning granularities, domain-level and instance-level, and propose \BiForget, an automated framework for synthesizing high-quality forget sets. Unlike prior work relying on \emph{external} generators, \BiForget exploits the target model per se to elicit data that matches its internal knowledge distribution through seed-guided and adversarial prompting. Our experiments across diverse benchmarks show that it achieves a superior balance of relevance, diversity, and efficiency. Quantitatively, in the Harry Potter domain, it improves relevance by ${\sim}20$ and diversity by ${\sim}$0.05 while \emph{halving} the total data size compared to SOTAs. Ultimately, it facilitates more robust forgetting and better utility preservation, providing a more rigorous foundation for evaluating LLM unlearning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04278 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning Xu, Xiaoyu Du, Minxin Li, Zitong Liang, Zi Guo, Zhibiao Zhang, Shiyu Hu, Peizhao Ye, Qingqing Hu, Haibo Computation and Language Artificial Intelligence Cryptography and Security Machine Learning Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfully represent the true ``forgetting scope'' learned by the model. We formalize two distinct unlearning granularities, domain-level and instance-level, and propose \BiForget, an automated framework for synthesizing high-quality forget sets. Unlike prior work relying on \emph{external} generators, \BiForget exploits the target model per se to elicit data that matches its internal knowledge distribution through seed-guided and adversarial prompting. Our experiments across diverse benchmarks show that it achieves a superior balance of relevance, diversity, and efficiency. Quantitatively, in the Harry Potter domain, it improves relevance by ${\sim}20$ and diversity by ${\sim}$0.05 while \emph{halving} the total data size compared to SOTAs. Ultimately, it facilitates more robust forgetting and better utility preservation, providing a more rigorous foundation for evaluating LLM unlearning. |
| title | From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning |
| topic | Computation and Language Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2601.04278 |