Language Models Get a Gender Makeover: Mitigating Gender Bias with
  Few-Shot Data Interventions
Himanshu Thakur Atishay Jain Praneetha Vaddamanu Paul Pu Liang Louis-Philippe Morency
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.