# μ2-Bench: A Multilingual Machine Unlearning Benchmark

arXiv: https://arxiv.org/abs/2609.20945 · HTML: https://mlsanity.com/paper/2609.20945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 18 of 18)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [TOFU: A Task of Fictitious Unlearning for LLMs](https://mlsanity.com/paper/2401.06121/) (2024)
- [Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning](https://mlsanity.com/paper/2404.05868/) (2024)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [Knowledge Unlearning for Mitigating Privacy Risks in Language Models](https://mlsanity.com/paper/2210.01504/) (2022)
- [MUSE: Machine Unlearning Six-Way Evaluation for Language Models](https://mlsanity.com/paper/2407.06460/) (2024)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics](https://mlsanity.com/paper/2506.12618/) (2025)
- [Multilingual Instruction Tuning With Just a Pinch of Multilinguality](https://mlsanity.com/paper/2401.01854/) (2024)
- [Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models](https://mlsanity.com/paper/2410.11718/) (2024)
- [Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models](https://mlsanity.com/paper/2406.12354/) (2024)
- [Learn and Unlearn: Addressing Misinformation in Multilingual LLMs](https://mlsanity.com/paper/2406.13748/) (2024)
- [Rethinking Cross-lingual Alignment: Balancing Transfer and Cultural Erasure in Multilingual LLMs](https://mlsanity.com/paper/2510.26024/) (2025)
- [Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs](https://mlsanity.com/paper/2601.05641/) (2026)
- [Evaluating Cross-Lingual Unlearning in Multilingual Language Models](https://mlsanity.com/paper/2601.06675/) (2026)
- [Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation](https://mlsanity.com/paper/2605.14404/) (2026)
- [Layer-Targeted Multilingual Knowledge Erasure in Large Language Models](https://mlsanity.com/paper/2602.22562/) (2026)

## Abstract (arXiv)

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 μ2-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 multiple languages. We show that successful MMU requires methods that reflect multilingual characteristics, and conduct analysis to provide deeper insights into MMU.