@inproceedings{jeung-etal-2026-dusk,
    title = "{DUSK}: Do Not Unlearn Shared Knowledge",
    author = "Jeung, Wonje  and
      Yoon, Sangyeon  and
      Hong, Hyesoo  and
      Kim, Soeun  and
      Han, Seungju  and
      Yu, Youngjae  and
      No, Albert",
    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.2085/",
    pages = "42013--42031",
    ISBN = "979-8-89176-395-1",
    abstract = "Machine unlearning aims to remove ``forget'' data while preserving knowledge from the ``retain'' data, yet a fundamental question arises when the two share content. By definition, an unlearned model should be indistinguishable from a model retrained solely on the retain set, which implies that shared knowledge must remain while only forget-specific content is removed. To evaluate this requirement, we introduce DUSK, the first benchmark for unlearning under realistic knowledge overlap. DUSK constructs documents containing both shared and unique knowledge and defines seven metrics to test whether methods erase forget-specific expressions without discarding shared facts. Evaluating nine recent approaches, we find that although surface text is often removed, current methods struggle to distinguish shared from unique knowledge, either erasing information that should be retained or failing to fully forget target content. DUSK provides a controlled, reproducible testbed for diagnosing these failures and guiding precise unlearning algorithms."
}