🌍 Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques

Abstract:
This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising results. Surprisingly, our findings reveal no performance disadvantages when applying these techniques to non-English languages. Using translations of the CrowS-Pairs dataset, our analysis identifies SentenceDebias as the best technique across different languages, reducing bias in mBERT by an average of 13%. We also find that debiasing techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses, particularly in lower-resource languages. These novel insights contribute to a deeper understanding of bias mitigation in multilingual language models and provide practical guidance for debiasing techniques in different language contexts.

Recommended citation: Reusens, M., Borchert, P., Mieskes, M., De Weerdt, J., & Baesens, B. (2023, December). Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 2887-2896).
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