@inproceedings{xu-etal-2026-domains,
    title = "From Domains to Instances: Dual-Granularity Data Synthesis for {LLM} Unlearning",
    author = "Xu, Xiaoyu  and
      Du, Minxin  and
      LI, Zitong  and
      Liang, Zi  and
      Guo, Zhibiao  and
      Shiyu, Zhang  and
      Hu, Peizhao  and
      Ye, Qingqing  and
      Hu, Haibo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1424/",
    pages = "28540--28556",
    ISBN = "979-8-89176-395-1",
    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 , an automated framework for synthesizing high-quality forget sets.Unlike prior work relying on \textit{external} generators, 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 \textit{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."
}