@inproceedings{bhavankar-etal-2025-neuroreset,
    title = "{N}euro{R}eset : {LLM} Unlearning via Dual Phase Mixed Methodology",
    author = "Bhavankar, Dhwani  and
      Sevalia, Het  and
      Agarwal, Shubh  and
      Kulkarni, Yogesh  and
      Walambe, Rahee  and
      Kotecha, Ketan",
    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-1.138/",
    pages = "1046--1051",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents the method for the unlearning of sensitive information from large language models as applied in the SemEval 2025 Task 4 challenge. The unlearning pipeline consists of two phases. In phase I, the model is instructed to forget specific datasets, and in phase II, the model is stabilized using a retention dataset. Unlearning with these methods secured a final score of 0.420 with the 2nd honorary mention in the 7B parameter challenge and a score of 0.36 in the 13th position for the 1B parameter challenge. The paper presents a background study, a brief literature review, and a gap analysis, as well as the methodology employed in our work titled NeuroReset. The training methodology and evaluation metrics are also presented, and the trade-offs between unlearning efficiency and model performance are discussed. The contributions of the paper are systematic unlearning, a comparative analysis of unlearning methods, and an empirical analysis of model performance post-unlearning."
}