@inproceedings{dong-etal-2025-undial,
    title = "{UNDIAL}: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models",
    author = "Dong, Yijiang River  and
      Lin, Hongzhou  and
      Belkin, Mikhail  and
      Huerta, Ramon  and
      Vuli{\'c}, Ivan",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.444/",
    pages = "8827--8840",
    ISBN = "979-8-89176-189-6",
    abstract = "Mitigating the retention of sensitive or private information in large language models is essential for enhancing privacy and safety. Existing unlearning methods, like Gradient Ascent and Negative Preference Optimization, directly tune models to remove unwanted information. However, these methods often become unstable because they fine-tune by maximizing loss, which is the opposite of traditional loss minimization in learning. This reversal creates instability, especially on larger datasets, as the model struggles to balance unlearning with maintaining language capacity, leading to over-unlearning. In this paper, we introduce UnDIAL (Unlearning via Self-Distillation on Adjusted Logits), a novel and robust unlearning method. Our approach leverages self-distillation to adjust logits and selectively reduce the influence of targeted tokens. This technique ensures smooth convergence and avoids catastrophic forgetting, even in challenging unlearning tasks with large datasets and sequential unlearning requests. Extensive experiments show that UnDIAL is the first direct tuning method to achieve both robustness in unlearning and scalability, while maintaining stable training dynamics and resilience to hyperparameter tuning."
}