Safety alignment evaluated only in English can hide serious vulnerabilities in other languages—culturally localized attacks often succeed where English ones fail, and template-based data augmentation is an effective way to scale safety training across low-resource languages.
This paper addresses the gap in multilingual safety training for large language models by creating MSafeTemp, a dataset of 60 languages built using template-based augmentation. The authors extract safe patterns from harmful prompts, translate them, and fill slots with culturally authentic content to generate diverse refusal and benign examples.