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arXiv · 2503.17632

FairFlow: Mitigating Dataset Biases through Undecided Learning

Jiali Cheng·Hadi Amiri
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Abstract

Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called ``FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases. The framework introduces two key components: a suite of data and model perturbation operations that generate different biased views of input samples, and a contrastive objective that learns debiased and robust representations from the resulting biased views of samples. Experiments show that FairFlow outperforms existing debiasing methods, particularly against out-of-domain and hard test samples without compromising the in-domain performance

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Jiali Cheng, Hadi Amiri. 2025-03-22. FairFlow: Mitigating Dataset Biases through Undecided Learning. https://arxiv.org/abs/2503.17632

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