@inproceedings{xu-etal-2025-zjuklab,
    title = "{ZJUKLAB} at {S}em{E}val-2025 Task 4: Unlearning via Model Merging.",
    author = "Xu, Haoming  and
      Wang, Shuxun  and
      Zhao, Yanqiu  and
      Zhong, Yi  and
      Jiang, Ziyan  and
      Zhao, Ningyuan  and
      Deng, Shumin  and
      Chen, Huajun  and
      Zhang, Ningyu",
    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-1.79/",
    pages = "566--574",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents the ZJUKLAB team{'}s submission for {\{}emph{\{}SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models{\}}{\}}. This task aims to selectively erase sensitive knowledge from large language models, avoiding both over-forgetting and under-forgetting issues. We propose an unlearning system that leverages Model Merging (specifically TIES-Merging), combining two specialized models into a more balanced unlearned model.Our system achieves competitive results, ranking {\{}textbf{\{}second among 26 teams{\}}{\}}, with an online score of 0.944 for Task Aggregate and 0.487 for overall Aggregate. In this paper, we also conduct local experiments and perform a comprehensive analysis of the unlearning process, examining performance trajectories, loss dynamics, and weight perspectives, along with several supplementary experiments, to understand the effectiveness of our method.Furthermore, we analyze the shortcomings of our method and evaluation metrics, emphasizing that MIA scores and ROUGE-based metrics alone are insufficient to fully evaluate successful unlearning. Finally, we emphasize the need for more comprehensive evaluation methodologies and rethinking of unlearning objectives in future research."
}