Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. In this work, we aim to answer the following research question: Can we reduce spurious correlations by modifying the ground truth labels of the training data? Specifically, we propose a simple yet effective debiasing framework, named Soft Label Encoding (SoftLE). We first train a teacher model with hard labels to determine each sample's degree of relying on shortcuts. We then add one dummy class to encode the shortcut degree, which is used to smooth other dimensions in the ground truth label to generate soft labels. This new ground truth label is used to train a more robust student model. Extensive experiments on two NLU benchmark tasks demonstrate that SoftLE significantly improves out-of-distribution generalization while maintaining satisfactory in-distribution accuracy.

翻译：近期研究表明，大型语言模型在自然语言理解任务中会依赖数据中的虚假相关性。本研究旨在回答以下研究问题：能否通过修改训练数据的真实标签来减少虚假相关性？具体而言，我们提出一种简单而有效的去偏框架——软标签编码。首先，我们使用硬标签训练一个教师模型，以确定每个样本对捷径的依赖程度。随后，我们添加一个虚拟类别来编码捷径程度，用于平滑真实标签中的其他维度以生成软标签。这种新的真实标签将被用于训练更鲁棒的学生模型。在两项自然语言理解基准任务上的大量实验表明，软标签编码在保持满意的分布内准确率的同时，显著提升了分布外泛化能力。