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arXiv · 2504.11183

Bias Beyond English: Evaluating Social Bias and Debiasing Methods in a Low-Resource Setting

Ej Zhou·Weiming Lu
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Abstract

Social bias in language models can potentially exacerbate social inequalities. Despite it having garnered wide attention, most research focuses on English data. In a low-resource scenario, the models often perform worse due to insufficient training data. This study aims to leverage high-resource language corpora to evaluate bias and experiment with debiasing methods in low-resource languages. We evaluated the performance of recent multilingual models in five languages: English, Chinese, Russian, Indonesian and Thai, and analyzed four bias dimensions: gender, religion, nationality, and race-color. By constructing multilingual bias evaluation datasets, this study allows fair comparisons between models across languages. We have further investigated three debiasing methods-CDA, Dropout, SenDeb-and demonstrated that debiasing methods from high-resource languages can be effectively transferred to low-resource ones, providing actionable insights for fairness research in multilingual NLP.

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Ej Zhou, Weiming Lu. 2025-04-15. Bias Beyond English: Evaluating Social Bias and Debiasing Methods in a Low-Resource Setting. https://arxiv.org/abs/2504.11183

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