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arXiv · 2505.16722

Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification

Himanshu Beniwal·Youngwoo Kim·Maarten Sap·Soham Dan·Thomas Hartvigsen
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Abstract

As large language models (LLMs) become increasingly prevalent in global applications, ensuring that they are toxicity-free across diverse linguistic contexts remains a critical challenge. We explore "Cross-lingual Detoxification", a cross-lingual paradigm that mitigates toxicity, enabling detoxification capabilities to transfer between high and low-resource languages across different script families. We analyze cross-lingual detoxification's effectiveness through 392 extensive settings to evaluate toxicity reduction in cross-distribution settings with limited data and investigate how mitigation impacts model performance on non-toxic tasks, revealing trade-offs between safety and knowledge preservation. Our code and dataset are publicly available at https://github.com/himanshubeniwal/Breaking-mBad.

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Himanshu Beniwal, Youngwoo Kim, Maarten Sap, Soham Dan, Thomas Hartvigsen. 2025-05-22. Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification. https://arxiv.org/abs/2505.16722

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