@inproceedings{ramakrishna-etal-2025-lume,
    title = "{LUME}: {LLM} Unlearning with Multitask Evaluations",
    author = "Ramakrishna, Anil  and
      Wan, Yixin  and
      Jin, Xiaomeng  and
      Chang, Kai-Wei  and
      Bu, Zhiqi  and
      Vinzamuri, Bhanukiran  and
      Cevher, Volkan  and
      Hong, Mingyi  and
      Gupta, Rahul",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.347/",
    doi = "10.18653/v1/2025.findings-emnlp.347",
    pages = "6524--6535",
    ISBN = "979-8-89176-335-7",
    abstract = "Unlearning aims to remove copyrighted, sensitive, or private content from large language models (LLMs) without a full retraining. In this work, we develop a multi-task unlearning benchmark LUME that features three tasks: (1) unlearn synthetically generated creative short novels, (2) unlearn synthetic biographies with sensitive information, and (3) unlearn a collection of public biographies. We further release two fine-tuned LLMs of 1B and 7B parameter sizes as the target models. We conduct detailed evaluations of several recently-proposed algorithms and present results on carefully crafted metrics to understand their behavior and limitations."
}