arXiv:2606.03291v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive facts, motivating unlearning methods that remove targeted knowledge without costly retraining. However, unlearning research remains heavily English-centric. We study multilingual unlearning by extending the TOFU benchmark to five languages, and fine-tune, unlearn, and query our models with different permutations of languages. We find that unlearning transfer, the ability of an unlearned model to "forget" facts in languages other than the unlearning language, is highly variable: e.g., it is stron
The proliferation of LLMs and increasing global concerns about data privacy and intellectual property are driving renewed focus on methods to control and manage their embedded knowledge.
Understanding multilingual unlearning is critical for deploying globally compliant and ethically responsible LLMs, particularly for entities operating across diverse linguistic and regulatory landscapes.
The ability to selectively 'forget' information across languages without full retraining allows for more agile and adaptable LLM deployment, potentially reducing compliance costs and addressing bias more effectively.
- · LLM developers
- · Multinational corporations
- · Privacy-focused organizations
- · AI ethicists
- · LLMs with poor unlearning transfer
- · Organizations relying on static models
- · English-centric AI research
Improved methods for targeted data removal in LLMs will enhance data privacy and intellectual property protection for users globally.
This research could lead to more robust regulatory frameworks for AI, as unlearning capabilities strengthen compliance with diverse international data and content laws.
The development of truly 'unforgettable' facts or persistent biases across languages could emerge as a new challenge, requiring novel architectural solutions or pre-training interventions.
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