| Challenge: | Existing studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. |
| Approach: | They propose to perform debiasing in a joint latent space rather than directly on LLM representations by using an autoencoder trained on parallel TED talk scripts. |
| Outcome: | The proposed method improves both the overall debiasing performance and cross-lingual transferability of the proposed techniques across four languages. |
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| Challenge: | Debiasing techniques that target sentence representations are being investigated in multilingual models . a growing interest in addressing bias detection and mitigation in NLP due to their societal implications. |
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Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation (2025.findings-acl)
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| Challenge: | Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages. |
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From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
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| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
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Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains (2026.findings-acl)
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Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
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| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
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Self-Supervised Position Debiasing for Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences. |
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| Challenge: | Existing studies show translation artifacts in translations influence performance of cross-lingual tasks. |
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Open-DeBias: Toward Mitigating Open-Set Bias in Language Models (2025.findings-emnlp)
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| Challenge: | Existing approaches to addressing harmful biases in LLMs are limited to predefined categories . a novel, data-efficient, and parameter-efficient debiasing method is proposed to mitigate existing social and stereotypical biase . |
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