@inproceedings{farashah-etal-2026-multilingual,
    title = "Multilingual Amnesia: On the Transferability of Unlearning in Multilingual {LLM}s",
    author = "Farashah, Alireza Dehghanpour  and
      Khandelwal, Aditi  and
      Fauchard, Marylou  and
      Shi, Zhuan  and
      Rostamzadeh, Negar  and
      Farnadi, Golnoosh",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.260/",
    pages = "5570--5589",
    ISBN = "979-8-89176-380-7",
    abstract = "As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing research on machine unlearning has mainly focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we address the problem of multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes into ten languages through translation{---}English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian{---}spanning five language families and varying resource levels. Our experiments show that unlearning in high-resource languages tends to be more stable, with asymmetric transfer observed between typologically related languages. Moreover, analysis of linguistic distances reveals that syntactic similarity is the most predictive factor of cross-lingual unlearning effects."
}