Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models

• URL: http://arxiv.org/abs/2307.10522v1
• Date: Thu, 20 Jul 2023 01:48:51 GMT
• Title: Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models
• Authors: Somayeh Ghanbarzadeh, Yan Huang, Hamid Palangi, Radames Cruz Moreno, and Hamed Khanpour
• Abstract summary: Existing solutions require debiasing training processes and datasets for debiasing. 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.
• Score: 9.534831387705312
• License: http://creativecommons.org/licenses/by/4.0/
• 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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