@inproceedings{wang-etal-2025-dutir,
    title = "{DUT}ir at {S}em{E}val-2025 Task 4: Optimized Fine-Tuning of Linear Layers for Balanced Knowledge Forgetting and Retention",
    author = "Wang, Zekun  and
      Zeng, Jingjie  and
      Li, Yingxu  and
      Yang, Liang  and
      Lin, Hongfei",
    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-1.72/",
    pages = "522--526",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our system used in SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. In this work, we propose a method for controlling the fine-tuning of a model{'}s linear layers, referred to as CTL-Finetune (Control-Tuned Linear Fine-tuning). The goal of our method is to allow the model to forget specific information while preserving the knowledge it needs to retain. The method consists of four main components: 1) shuffling data labels, 2) shuffling label gradient calculation, 3) determination of control layers, and 4) fine-tuning using a combination of gradient ascent and gradient descent. Experimental results demonstrate that our approach effectively enables the model to forget targeted knowledge while minimizing the impact on retained information, thus maintaining the model{'}s overall performance."
}