Mitigating social biases of pre-trained language models via contrastive self-debiasing with double data augmentation-Reference-Cited by-同舟云学术

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Mitigating social biases of pre-trained language models via contrastive self-debiasing with double data augmentation

Published:2024-07 Issue: Volume:332 Page:104143
ISSN:0004-3702
Container-title:Artificial Intelligence
language:en
Short-container-title:Artificial Intelligence

Author:

Li Yingji^ORCID,Du Mengnan^ORCID,Song Rui,Wang Xin,Sun Mingchen,Wang Ying^ORCID

Publisher

Elsevier BV

Reference72 articles.

1. Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning;Su;Artif. Intell.,2021

2. Mitigating demographic bias in ai-based resume filtering;Deshpande,2020

3. Dissecting racial bias in an algorithm used to manage the health of populations;Obermeyer;Science,2019

4. Word embeddings quantify 100 years of gender and ethnic stereotypes;Garg;Proc. Natl. Acad. Sci.,2018

5. Quantifying and alleviating political bias in language models;Liu;Artif. Intell.,2022

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