Iterative Multilingual Spectral Attribute Erasure

• URL: http://arxiv.org/abs/2506.11244v1
• Date: Thu, 12 Jun 2025 19:30:30 GMT
• Title: Iterative Multilingual Spectral Attribute Erasure
• Authors: Shun Shao, Yftah Ziser, Zheng Zhao, Yifu Qiu, Shay B. Cohen, Anna Korhonen,
• Abstract summary: Iterative Multilingual Spectral Attribute Erasure (IMSAE)<n>We present Iterative Multilingual Spectral Attribute Erasure (IMSAE), which identifies and mitigates joint bias subspaces across multiple languages.
• Score: 36.73678940946656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual representations embed words with similar meanings to share a common semantic space across languages, creating opportunities to transfer debiasing effects between languages. However, existing methods for debiasing are unable to exploit this opportunity because they operate on individual languages. We present Iterative Multilingual Spectral Attribute Erasure (IMSAE), which identifies and mitigates joint bias subspaces across multiple languages through iterative SVD-based truncation. Evaluating IMSAE across eight languages and five demographic dimensions, we demonstrate its effectiveness in both standard and zero-shot settings, where target language data is unavailable, but linguistically similar languages can be used for debiasing. Our comprehensive experiments across diverse language models (BERT, LLaMA, Mistral) show that IMSAE outperforms traditional monolingual and cross-lingual approaches while maintaining model utility.

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