New MSafeTemp dataset advances multilingual LLM safety alignment
Original title: How I Aligned Your Model: Scaling Up Multilingual Safety Training with Templates
The template-based pipeline produces a 60-language corpus for safety training, exposing gaps in English-only evaluation.
Current safety alignment for large language models focuses heavily on English, limiting performance in multilingual settings especially low-resource languages. The authors introduce a scalable template-based augmentation pipeline that extracts safe templates from harmful prompts, translates them, and fills slots with culturally authentic entities to create massive refusal and benign corpora. This yields MSafeTemp, a validated dataset spanning 60 languages. Experiments confirm that English-only testing misjudges multilingual safety, with culturally localized variants revealing additional failures and template augmentation improving overall alignment quality and diversity.
- MSafeTemp spans 60 languages
- Template pipeline extracts and translates safe templates
- English-only evaluation misses translated and localized attacks
- Improves safety performance beyond English
From the original article
1 min read at huggingface.co
Safety alignment for Large Language Models is heavily focused on English with limited multilingual representation, particularly damaging low-resource language performance. Related open training datasets are often limited in size or do not follow region-localized harmful topics to broaden the scope of alignment beyond cultures.
By Max Neuwinger, Hanna Yukhymenko, Imanol Schlag · huggingface.co
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