$μ^2$-Bench: A Multilingual Machine Unlearning Benchmark
Undesired information such as harmful content and private data propagates through Multilingual Large Language Models (LLMs) via direct training and indirect cross-linguistic spread. Multilingual Machine Unlearning (MMU) aims to remove such information, yet its evaluation remains underexplored, leaving unclear whether unlearning truly eliminates target knowledge across all languages. To bridge this gap, we introduce $μ^2$-Bench, an MMU benchmark that simulates the full pipeline of memorization, unlearning, and evaluation across diverse languages. It 1) spans a broad set of languages, 2) evaluates on both training and hold-out languages, and 3) assesses knowledge as dispersed across multipl...
| Authors | Kyomin Hwang, Hyeonjin Kim, Hyunho Lee et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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