@inproceedings{bronec-helcl-2026-thesis,
    title = "Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models",
    author = "Bronec, Jan  and
      Helcl, Jind{\v{r}}ich",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.49/",
    pages = "554--562",
    ISBN = "979-8-89176-393-7",
    abstract = "As large language models (LLM) trained on massive corpora scraped from the web exhibit the capability to reproduce sensitive and copyright-protected data, the field of machine unlearning has emerged to address the arising ethical and legal concerns.While previous research has provided a unified evaluation of LLM unlearning methods, this unification remains constrained to English-only models and datasets.We aim to address the prevailing fragmentation in recent cross-lingual unlearning research by extending existing unified benchmarks with multilingual data.To that end, we plan to compile a dataset of parallel translations of question-answer pairs consisting of real-world facts and synthetic personally identifiable information.Moreover, we will focus on mitigating model degradation during unlearning by selectively editing only those layers that contain the given knowledge."
}