Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention (2022.emnlp-main)
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| Challenge: | Recent research has exposed text encoders for replicating discriminatory social biases which may cause unintended and undesired model behaviors with respect to social groups. |
| Approach: | They propose a method to reduce social stereotypes by redistributing attention scores of a text encoder so it forgets any preference to historically advantaged groups and attends to all social classes with the same intensity. |
| Outcome: | The proposed method reduces stereotypes and inflicts no semantic damage on pre-trained encoders. |
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Weicheng Ma, Henry Scheible, Brian Wang, Goutham Veeramachaneni, Pratim Chowdhary, Alan Sun, Andrew Koulogeorge, Lili Wang, Diyi Yang, Soroush Vosoughi
| Challenge: | Current approaches for examining stereotypes in PLMs require intricate human knowledge about these stereotypes and entail careful manual curation of examples. |
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When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation (2026.findings-acl)
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| Challenge: | Preprocessing-based methods for stereotype mitigation are widely used in NLP . preprocessing methods cause unintended shifts in attention flow, authors say . |
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Towards Debiasing Sentence Representations (2020.acl-main)
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Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Recent work has shown word-level embeddings reflect and propagate social biases present in training corpora. |
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Debiasing Pre-trained Contextualised Embeddings (2021.eacl-main)
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| Challenge: | a study of contextualised word embeddings shows discriminative biases are encoded in contextualised embeddables. |
| Approach: | They propose a fine-tuning method that can be applied at token- or sentence-levels to debias pre-trained contextualised embeddings. |
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Gender-preserving Debiasing for Pre-trained Word Embeddings (P19-1)
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| Challenge: | Existing methods for debiasing word embeddings have shown discriminative biases . word embeds learnt from social media have shown to encode racist, offensive and discriminative language usage. |
| Approach: | They propose a method that preserves gender-related information while removing stereotypical gender biases from pre-trained word embeddings. |
| Outcome: | The proposed method preserves gender-related information while removing stereotypical discriminative gender biases from pre-trained word embeddings. |
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization (2024.emnlp-main)
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| Challenge: | Existing methods for debiasing depend on attribute labels and target attributes. |
| Approach: | They propose a method that uses class-wise variance of embeddings to reduce the effects of debiasing on a downstream task. |
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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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| Challenge: | Existing methods to learn representations from text often reflect social biases . previous methods rely on pre-specified direction or suffer from unstable training . |
| Approach: | They propose an adversarial disentangled debiasing model to decouple social bias attributes from intermediate representations trained on the main task. |
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Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP (2021.tacl-1)
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| Challenge: | Pretrained language models pick up and reproduce undesirable biases when trained on large, unfiltered crawls from the Internet. |
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Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (N19-1)
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| Challenge: | Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases. |
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