@inproceedings{reisizadeh-etal-2026-blur,
    title = "{BLUR}: A Bi-Level Optimization Approach for {LLM} Unlearning",
    author = "Reisizadeh, Hadi  and
      Jia, Jinghan  and
      Bu, Zhiqi  and
      Vinzamuri, Bhanukiran  and
      Ramakrishna, Anil  and
      Chang, Kai-Wei  and
      Cevher, Volkan  and
      Liu, Sijia  and
      Hong, Mingyi",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.331/",
    pages = "7043--7058",
    ISBN = "979-8-89176-380-7",
    abstract = "Enabling large language models (LLMs) to unlearn knowledge and capabilities acquired during training has proven vital for ensuring compliance with data regulations and promoting ethical practices in generative AI. Although there are growing interests in developing various unlearning algorithms, it remains unclear how to best formulate the unlearning problem. The most popular formulation uses a weighted sum of forget and retain loss, but it often leads to performance degradation due to the inherent trade-off between forget and retain losses. In this work, we argue that it is important to model the hierarchical structure of the unlearning problem, where the forget problem (which \textit{unlearns} certain knowledge and/or capabilities) takes priority over the retain problem (which preserves model utility). This hierarchical structure naturally leads to a bi-level optimization formulation where the lower-level objective focuses on minimizing the forget loss, while the upper-level objective aims to maintain the model{'}s utility. Based on this new formulation, we propose a novel algorithm, termed Bi-Level UnleaRning (), which not only possesses strong theoretical guarantees but more importantly, delivers superior performance. In particular, our extensive experiments demonstrate that consistently outperforms all the state-of-the-art algorithms across various unlearning tasks, models, and metrics."
}