Pretrained language models memorize vast amounts of information, including
private and copyrighted data, raising significant safety concerns. Retraining
these models after excluding sensitive data is prohibitively expensive, making
machine unlearning a viable, cost-effective alternative. Previous research has
focused on machine unlearning for monolingual models, but we find that
unlearning in one language does not necessarily transfer to others. This
vulnerability makes models susceptible to low-resource language attacks, where
sensitive information remains accessible in less dominant languages. This paper
presents a pioneering approach to machine unlearning for multilingual language
models, selectively erasing information across different languages while
maintaining overall performance. Specifically, our method employs an adaptive
unlearning scheme that assigns language-dependent weights to address different
language performances of multilingual language models. Empirical results
demonstrate the effectiveness of our framework compared to existing unlearning
baselines, setting a new standard for secure and adaptable multilingual
language models.

本研究论文提出了一种创新的方法，针对多语言语言模型的机器遗忘，通过选择性地擦除不同语言中的信息，同时保持总体性能，有效解决了低资源语言攻击的问题，为安全可适应的多语言语言模型设定了新的标准。