Papers with iterative SVD-based truncation

1 papers
Iterative Multilingual Spectral Attribute Erasure (2025.emnlp-main)

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Challenge: Existing methods for debiasing are unable to exploit this opportunity because they operate on individual languages.
Approach: They propose to iterate multilingual spectral attribute error (IMSAE) to mitigate joint bias subspaces across multiple languages through iterative SVD-based truncation.
Outcome: The proposed method outperforms monolingual and cross-lingual approaches while maintaining model utility.

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