Multilingual Safety Signals Are Multi-Layered: Filtering Safety-Degrading Data for Safer LLMs

The paper introduces MMSAFE, a multi-layer framework designed to identify safety-degrading data in multilingual large language models. It shows that safety signals are distributed across multiple layers and only partially shared across languages, unlike the single-layer assumption used in monolingual settings. Experiments demonstrate that MMSAFE reduces harmful-response rates by 60% compared to random filtering and outperforms the best single-layer baseline across various models, languages, and safety benchmarks.

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• llms
• fine-tuning
• benchmarks
• safety