Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions

• URL: http://arxiv.org/abs/2306.04597v1
• Date: Wed, 7 Jun 2023 16:50:03 GMT
• Title: Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions
• Authors: Himanshu Thakur, Atishay Jain, Praneetha Vaddamanu, Paul Pu Liang and Louis-Philippe Morency
• Abstract summary: Societal biases present in pre-trained large language models are a critical issue. We propose data intervention strategies as a powerful yet simple technique to reduce gender bias in pre-trained models.
• Score: 50.67412723291881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Societal biases present in pre-trained large language models are a critical issue as these models have been shown to propagate biases in countless downstream applications, rendering them unfair towards specific groups of people. Since large-scale retraining of these models from scratch is both time and compute-expensive, a variety of approaches have been previously proposed that de-bias a pre-trained model. While the majority of current state-of-the-art debiasing methods focus on changes to the training regime, in this paper, we propose data intervention strategies as a powerful yet simple technique to reduce gender bias in pre-trained models. Specifically, we empirically show that by fine-tuning a pre-trained model on only 10 de-biased (intervened) training examples, the tendency to favor any gender is significantly reduced. Since our proposed method only needs a few training examples, our few-shot debiasing approach is highly feasible and practical. Through extensive experimentation, we show that our debiasing technique performs better than competitive state-of-the-art baselines with minimal loss in language modeling ability.

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