@inproceedings{premptis-etal-2025-ails,
    title = "{AILS}-{NTUA} at {S}em{E}val-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking",
    author = "Premptis, Iraklis  and
      Lymperaiou, Maria  and
      Filandrianos, George  and
      Menis Mastromichalakis, Orfeas  and
      Voulodimos, Athanasios  and
      Stamou, Giorgos",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.184/",
    pages = "1383--1405",
    ISBN = "979-8-89176-273-2",
    abstract = "The {\{}textit{\{}Unlearning Sensitive Content from Large Language Models{\}}{\}} task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our work, we leverage parameter-efficient, gradient-based unlearning using low-rank (LoRA) adaptation and layer-focused fine-tuning. To further enhance unlearning effectiveness, we employ data chunking, splitting forget data into disjoint partitions and merging them with cyclically sampled retain samples at a pre-defined ratio. Our task-agnostic method achieves an outstanding forget-retain balance, ranking first on leaderboards and significantly outperforming baselines and competing systems."
}