CREST: Universal Safety Guardrails Through Cluster-Guided Cross-Lingual Transfer

• URL: http://arxiv.org/abs/2512.02711v1
• Date: Tue, 02 Dec 2025 12:41:48 GMT
• Title: CREST: Universal Safety Guardrails Through Cluster-Guided Cross-Lingual Transfer
• Abstract summary: CREST is a parameter-efficient multilingual safety classification model that supports 100 languages with only 0.5B parameters.<n>By training on a strategically chosen subset of only 13 high-resource languages, our model utilizes cluster-based cross-lingual transfer.<n>This approach addresses the challenge of limited training data in low-resource settings.
• Score: 0.5729426778193399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring content safety in large language models (LLMs) is essential for their deployment in real-world applications. However, existing safety guardrails are predominantly tailored for high-resource languages, leaving a significant portion of the world's population underrepresented who communicate in low-resource languages. To address this, we introduce CREST (CRoss-lingual Efficient Safety Transfer), a parameter-efficient multilingual safety classification model that supports 100 languages with only 0.5B parameters. By training on a strategically chosen subset of only 13 high-resource languages, our model utilizes cluster-based cross-lingual transfer from a few to 100 languages, enabling effective generalization to both unseen high-resource and low-resource languages. This approach addresses the challenge of limited training data in low-resource settings. We conduct comprehensive evaluations across six safety benchmarks to demonstrate that CREST outperforms existing state-of-the-art guardrails of comparable scale and achieves competitive results against models with significantly larger parameter counts (2.5B parameters and above). Our findings highlight the limitations of language-specific guardrails and underscore the importance of developing universal, language-agnostic safety systems that can scale effectively to serve global populations.

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