@inproceedings{salas-jimenez-etal-2025-gil,
    title = "{GIL}-{IIMAS} {UNAM} at {S}em{E}val-2025 Task 4: {LA}-Min({E}): {LLM} Unlearning Approaches Under Function Minimizing Evaluation Constraints",
    author = "Salas - Jimenez, Karla  and
      L{\'o}pez - Ponce, Francisco  and
      Hern{\'a}ndez - Bustamante, Diego  and
      Bel - Enguix, Gemma  and
      G{\'o}mez - Adorno, Helena",
    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-1.205/",
    pages = "1557--1562",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes Gradient Ascent and Task Vectors as LLM unlearning methodologies applied to SemEval 2025{'}s task 4. This task focuses on LLM unlearning on specific information under the constraints of preserving the model{'}s advanced text generation capabilities; meaning that our implementations of these algorithms were constrained both in the information datasets as well as the overall effect of each algorithm in the model{'}s general performance. Our implementation produced modified language models that ranked 7th out of 14 valid participants in the 7B parameter model, and 6th out of 24 in the 1B parameter model."
}