$μ^2$-Bench: A Multilingual Machine Unlearning Benchmark

2026-09-17

AI Summary

为解决多语言大模型中不良信息传播问题，提出μ²-Bench基准，通过模拟记忆、遗忘和评估过程来测试跨语言信息消除效果。

Abstract

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

Problem

• Multilingual Machine Unlearning
• Undesired Information
• Cross-linguistic Spread

Innovation

• Multilingual Machine Unlearning
• Benchmark
• Cross-linguistic Spread
• Knowledge Dispersal