Evaluating Cross-Lingual Unlearning in Multilingual Language Models

• URL: http://arxiv.org/abs/2601.06675v1
• Date: Sat, 10 Jan 2026 20:27:32 GMT
• Title: Evaluating Cross-Lingual Unlearning in Multilingual Language Models
• Authors: Tyler Lizzo, Larry Heck,
• Abstract summary: Subspace-projection achieves strong cross-lingual forgetting with minimal degradation.<n>We show that multilingual forgetting depends on geometry in weight space, motivating subspace-based approaches for future unlearning systems.
• Score: 7.530890774798437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the first comprehensive evaluation of cross-lingual unlearning in multilingual LLMs. Using translated TOFU benchmarks in seven language/script variants, we test major unlearning algorithms and show that most fail to remove facts outside the training language, even when utility remains high. However, subspace-projection consistently outperforms the other methods, achieving strong cross-lingual forgetting with minimal degradation. Analysis of learned task subspaces reveals a shared interlingua structure: removing this shared subspace harms all languages, while removing language-specific components selectively affects one. These results demonstrate that multilingual forgetting depends on geometry in weight space, motivating subspace-based approaches for future unlearning systems.

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