FairFlow: Mitigating Dataset Biases through Undecided Learning

• URL: http://arxiv.org/abs/2503.17632v1
• Date: Sat, 22 Mar 2025 03:35:51 GMT
• Title: FairFlow: Mitigating Dataset Biases through Undecided Learning
• Authors: Jiali Cheng, Hadi Amiri,
• Abstract summary: Language models are prone to dataset biases, known as shortcuts and spurious correlations in data.<n>We present a new debiasing framework called FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions.
• Score: 14.755831733659699
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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