From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

• URL: http://arxiv.org/abs/2601.04278v1
• Date: Wed, 07 Jan 2026 12:41:07 GMT
• Title: From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning
• Authors: Xiaoyu Xu, Minxin Du, Zitong Li, Zi Liang, Zhibiao Guo, Shiyu Zhang, Peizhao Hu, Qingqing Ye, Haibo Hu,
• Abstract summary: BiForget is an automated framework for synthesizing high-quality forget sets.<n>It exploits the target model per se to elicit data that matches its internal knowledge distribution.<n>It achieves a superior balance of relevance, diversity, and efficiency.
• Score: 22.118481589256632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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 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.

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