Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions
This addresses fairness issues in AI for users affected by biased language models, offering a practical and incremental improvement over existing debiasing methods.
The paper tackles gender bias in pre-trained large language models by proposing few-shot data interventions, showing that fine-tuning on only 10 de-biased examples significantly reduces gender bias while maintaining language modeling ability and outperforming state-of-the-art baselines.
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.