Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914108542025728 |
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| author | Beniwal, Himanshu Kim, Youngwoo Sap, Maarten Dan, Soham Hartvigsen, Thomas |
| author_facet | Beniwal, Himanshu Kim, Youngwoo Sap, Maarten Dan, Soham Hartvigsen, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification Beniwal, Himanshu Kim, Youngwoo Sap, Maarten Dan, Soham Hartvigsen, Thomas Computation and Language Artificial Intelligence 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. |
| title | Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.16722 |