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. |
| Approach: | They propose a method to debias word embeddings to reduce biases at sentence level . they hope their work will inspire future research on characterizing and removing biase . |
| Outcome: | The proposed method reduces biases and preserves performance on downstream tasks such as sentiment analysis and natural language understanding. |
Similar Papers
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
Copied to clipboard
| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
| Approach: | They propose an algorithm that uses a neural network to perform ‘soft debiasing’ and build on the seminal work of (CITATION) and (CitATION). |
| Outcome: | The proposed algorithm outperforms current methods on gender, race, and religion metrics on a wide range of metrics. |
Debiasing Pre-trained Contextualised Embeddings (2021.eacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed method can be applied at token- or sentence-levels to debias pre-trained models without requiring retrains. |
Bias and Fairness in Natural Language Processing (D19-2)
Copied to clipboard
| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Open-DeBias: Toward Mitigating Open-Set Bias in Language Models (2025.findings-emnlp)
Copied to clipboard
| 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 . |
| Approach: | They propose an open-set bias detection and mitigation method to address harmful biases in text-based QA. |
| Outcome: | The proposed method improves QA accuracy on Korean BBQ dataset by nearly 48% on ambiguous subsets and 6% on disambiguated ones. |
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021.acl-long)
Copied to clipboard
| Challenge: | Recent work has focused on measuring and mitigating bias in pretrained language models. |
| Approach: | They propose a dataset that measures and mitigates bias across gender,race, religion, and queerness . they compare REDDITBIAS to a widely used conversational DialoGPT model . |
| Outcome: | The proposed framework measures and mitigates bias across gender,race, religion, and queerness dimensions. |
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (N19-1)
Copied to clipboard
| 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. |
| Approach: | They propose a method to debias word embeddings in multiclass settings such as gender and religion, extending the work of Bolukbasi et al. (2016). |
| Outcome: | The proposed method maintains the efficacy in standard NLP tasks while maintaining the utility of embeddings. |
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention (2022.emnlp-main)
Copied to clipboard
| 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. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
Copied to clipboard
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Mitigating gender bias in NLP has a long history tied to debiasing static word embeddings. |
| Approach: | They propose a masked language modelling task where content is developed around known social stereotypes and a projective debiasing method is used to reduce bias. |
| Outcome: | The proposed methods reduce intrinsic bias and mitigat observed bias in a downstream setting, but the two outcomes are not necessarily correlated. |
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (N19-1)
Copied to clipboard
| Challenge: | Existing methods to remove gender bias from word embeddings are insufficient, we argue . existing methods for gender-neutral modeling are ineffective, we conclude . |
| Approach: | They propose methods to reduce gender bias in word embeddings by debiasing them using text corpora. |
| Outcome: | The proposed methods show that they can reduce gender bias in word embeddings . the proposed methods are insufficient and should not be trusted, the authors argue . |