@inproceedings{ghanbarzadeh-etal-2023-gender,
title = "Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models",
author = "Ghanbarzadeh, Somayeh and
Huang, Yan and
Palangi, Hamid and
Cruz Moreno, Radames and
Khanpour, Hamed",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.336",
doi = "10.18653/v1/2023.findings-acl.336",
pages = "5448--5458",
abstract = "Recent studies have revealed that the widely-used Pre-trained Language Models (PLMs) propagate societal biases from the large unmoderated pre-training corpora. Existing solutions require debiasing training processes and datasets for debiasing, which are resource-intensive and costly. Furthermore, these methods hurt the PLMs{'} performance on downstream tasks. In this study, we propose Gender-tuning, which debiases the PLMs through fine-tuning on downstream tasks{'} datasets. For this aim, Gender-tuning integrates Masked Language Modeling (MLM) training objectives into fine-tuning{'}s training process. Comprehensive experiments show that Gender-tuning outperforms the state-of-the-art baselines in terms of average gender bias scores in PLMs while improving PLMs{'} performance on downstream tasks solely using the downstream tasks{'} dataset. Also, Gender-tuning is a deployable debiasing tool for any PLM that works with original fine-tuning.",
}
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<abstract>Recent studies have revealed that the widely-used Pre-trained Language Models (PLMs) propagate societal biases from the large unmoderated pre-training corpora. Existing solutions require debiasing training processes and datasets for debiasing, which are resource-intensive and costly. Furthermore, these methods hurt the PLMs’ performance on downstream tasks. In this study, we propose Gender-tuning, which debiases the PLMs through fine-tuning on downstream tasks’ datasets. For this aim, Gender-tuning integrates Masked Language Modeling (MLM) training objectives into fine-tuning’s training process. Comprehensive experiments show that Gender-tuning outperforms the state-of-the-art baselines in terms of average gender bias scores in PLMs while improving PLMs’ performance on downstream tasks solely using the downstream tasks’ dataset. Also, Gender-tuning is a deployable debiasing tool for any PLM that works with original fine-tuning.</abstract>
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%0 Conference Proceedings
%T Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models
%A Ghanbarzadeh, Somayeh
%A Huang, Yan
%A Palangi, Hamid
%A Cruz Moreno, Radames
%A Khanpour, Hamed
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ghanbarzadeh-etal-2023-gender
%X Recent studies have revealed that the widely-used Pre-trained Language Models (PLMs) propagate societal biases from the large unmoderated pre-training corpora. Existing solutions require debiasing training processes and datasets for debiasing, which are resource-intensive and costly. Furthermore, these methods hurt the PLMs’ performance on downstream tasks. In this study, we propose Gender-tuning, which debiases the PLMs through fine-tuning on downstream tasks’ datasets. For this aim, Gender-tuning integrates Masked Language Modeling (MLM) training objectives into fine-tuning’s training process. Comprehensive experiments show that Gender-tuning outperforms the state-of-the-art baselines in terms of average gender bias scores in PLMs while improving PLMs’ performance on downstream tasks solely using the downstream tasks’ dataset. Also, Gender-tuning is a deployable debiasing tool for any PLM that works with original fine-tuning.
%R 10.18653/v1/2023.findings-acl.336
%U https://aclanthology.org/2023.findings-acl.336
%U https://doi.org/10.18653/v1/2023.findings-acl.336
%P 5448-5458
Markdown (Informal)
[Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models](https://aclanthology.org/2023.findings-acl.336) (Ghanbarzadeh et al., Findings 2023)
ACL