@inproceedings{peng-etal-2025-debiasing,
title = "Debiasing Multilingual {LLM}s in Cross-lingual Latent Space",
author = "Peng, Qiwei and
Hu, Guimin and
Chai, Yekun and
S{\o}gaard, Anders",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1149/",
pages = "22593--22604",
ISBN = "979-8-89176-332-6",
abstract = "Debiasing techniques such as SentDebias aim to reduce bias in large language models (LLMs). Previous studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. In this work, we therefore propose to perform debiasing in a joint latent space rather than directly on LLM representations. We construct a well-aligned cross-lingual latent space using an autoencoder trained on parallel TED talk scripts. Our experiments with Aya-expanse and two debiasing techniques across four languages (English, French, German, Dutch) demonstrate that a) autoencoders effectively construct a well-aligned cross-lingual latent space, and b) applying debiasing techniques in the learned cross-lingual latent space significantly improves both the overall debiasing performance and cross-lingual transferability."
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<abstract>Debiasing techniques such as SentDebias aim to reduce bias in large language models (LLMs). Previous studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. In this work, we therefore propose to perform debiasing in a joint latent space rather than directly on LLM representations. We construct a well-aligned cross-lingual latent space using an autoencoder trained on parallel TED talk scripts. Our experiments with Aya-expanse and two debiasing techniques across four languages (English, French, German, Dutch) demonstrate that a) autoencoders effectively construct a well-aligned cross-lingual latent space, and b) applying debiasing techniques in the learned cross-lingual latent space significantly improves both the overall debiasing performance and cross-lingual transferability.</abstract>
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%0 Conference Proceedings
%T Debiasing Multilingual LLMs in Cross-lingual Latent Space
%A Peng, Qiwei
%A Hu, Guimin
%A Chai, Yekun
%A Søgaard, Anders
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F peng-etal-2025-debiasing
%X Debiasing techniques such as SentDebias aim to reduce bias in large language models (LLMs). Previous studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. In this work, we therefore propose to perform debiasing in a joint latent space rather than directly on LLM representations. We construct a well-aligned cross-lingual latent space using an autoencoder trained on parallel TED talk scripts. Our experiments with Aya-expanse and two debiasing techniques across four languages (English, French, German, Dutch) demonstrate that a) autoencoders effectively construct a well-aligned cross-lingual latent space, and b) applying debiasing techniques in the learned cross-lingual latent space significantly improves both the overall debiasing performance and cross-lingual transferability.
%U https://aclanthology.org/2025.emnlp-main.1149/
%P 22593-22604
Markdown (Informal)
[Debiasing Multilingual LLMs in Cross-lingual Latent Space](https://aclanthology.org/2025.emnlp-main.1149/) (Peng et al., EMNLP 2025)
ACL
- Qiwei Peng, Guimin Hu, Yekun Chai, and Anders Søgaard. 2025. Debiasing Multilingual LLMs in Cross-lingual Latent Space. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22593–22604, Suzhou, China. Association for Computational Linguistics.