@inproceedings{vasilev-etal-2025-unilogit,
    title = "Unilogit: Robust Machine Unlearning for {LLM}s Using Uniform-Target Self-Distillation",
    author = "Vasilev, Stefan  and
      Herold, Christian  and
      Liao, Baohao  and
      Hashemi, Seyyed Hadi  and
      Khadivi, Shahram  and
      Monz, Christof",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1154/",
    pages = "22453--22472",
    ISBN = "979-8-89176-256-5",
    abstract = "This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specific information while maintaining overall model utility, a critical task in compliance with data privacy regulations like GDPR. Unlike prior methods that rely on static hyperparameters or starting model outputs, Unilogit dynamically adjusts target logits to achieve a uniform probability for the target token, leveraging the current model{'}s outputs for more accurate self-distillation targets. This approach not only eliminates the need for additional hyperparameters but also enhances the model{'}s ability to approximate the golden targets. Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit{'}s superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. Our analysis further reveals Unilogit{'}s robustness across various scenarios, highlighting its practical applicability and effectiveness in achieving efficacious machine unlearning."
}