Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting

• URL: http://arxiv.org/abs/2110.05367v3
• Date: Fri, 30 Jun 2023 14:52:42 GMT
• Title: Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
• Authors: Zahra Fatemi, Chen Xing, Wenhao Liu, Caiming Xiong
• Abstract summary: Forgetting information in the original training data may damage the model's downstream performance by a large margin. We propose GEnder Equality Prompt (GEEP) to improve gender fairness of pre-trained models with less forgetting.
• Score: 88.83117372793737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing studies addressing gender bias of pre-trained language models, usually build a small gender-neutral data set and conduct a second phase pre-training on the model with such data. However, given the limited size and concentrated focus of the gender-neutral data, catastrophic forgetting would occur during second-phase pre-training. Forgetting information in the original training data may damage the model's downstream performance by a large margin. In this work, we empirically show that catastrophic forgetting occurs in such methods by evaluating them with general NLP tasks in GLUE. Then, we propose a new method, GEnder Equality Prompt (GEEP), to improve gender fairness of pre-trained models with less forgetting. GEEP freezes the pre-trained model and learns gender-related prompts with gender-neutral data. Empirical results show that GEEP not only achieves SOTA performances on gender fairness tasks, but also forgets less and performs better on GLUE by a large margin.

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