Evaluating Cross-Lingual Unlearning in Multilingual Language Models

Tyler Lizzo; Larry Heck

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arXiv · 2601.06675

Evaluating Cross-Lingual Unlearning in Multilingual Language Models

Tyler Lizzo·Larry Heck

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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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Tyler Lizzo, Larry Heck. 2026-01-10. Evaluating Cross-Lingual Unlearning in Multilingual Language Models. https://arxiv.org/abs/2601.06675

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