@inproceedings{chen-etal-2025-ynu-semeval,
    title = "{YNU} at {S}em{E}val-2025 Task 4: Synthetic Token Alternative Training for {LLM} Unlearning",
    author = "Chen, Yang  and
      Luo, Zheyang  and
      Tang, Zhiwen",
    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/transition-to-people-yaml/2025.semeval-1.264/",
    pages = "2038--2043",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our system submitted to SemEval-2025 Task 4, which introduces the Synthetic Token Alternative Training (STAT) algorithm for efficient unlearning in large language models (LLMs). The proposed method aims to enable pretrained models to selectively forget designated data (the forget set) while preserving performance on the remaining data (the retain set).The STAT framework adopts a dual-stage process. In the first stage, pseudo tokens are generated through random sampling and applied to the forget set, facilitating more effective targeted unlearning. In the second stage, the model undergoes gradient-based optimization using an alternative training scheme that alternates between pseudo-token-augmented samples from the forget set and unmodified samples from the retain set. This design promotes stable unlearning of the specified data while accelerating convergence and preserving the model{'}s general performance.Our system achieved 3rd place in the 7B model track (OLMo-7B) and 7th place in the 1B model track (OLMo-1B), demonstrating substantial improvements over the official baselines, exhibiting superior stability in knowledge retention and more effective targeted forgetting compared to existing approaches."
}