Language models (LMs) have gained great achievement in various NLP tasks for
both fine-tuning and in-context learning (ICL) methods. Despite its outstanding
performance, evidence shows that spurious correlations caused by imbalanced
label distributions in training data (or exemplars in ICL) lead to robustness
issues. However, previous studies mostly focus on word- and phrase-level
features and fail to tackle it from the concept level, partly due to the lack
of concept labels and subtle and diverse expressions of concepts in text. In
this paper, we first use the LLM to label the concept for each text and then
measure the concept bias of models for fine-tuning or ICL on the test data.
Second, we propose a data rebalancing method to mitigate the spurious
correlations by adding the LLM-generated counterfactual data to make a balanced
label distribution for each concept. We verify the effectiveness of our
mitigation method and show its superiority over the token removal method.
Overall, our results show that there exist label distribution biases in
concepts across multiple text classification datasets, and LMs will utilize
these shortcuts to make predictions in both fine-tuning and ICL methods.

我们使用语言模型为每个文本标记概念并测量模型在测试数据上的概念偏差，然后提出一种数据再平衡方法来减轻由于训练数据中的不平衡标签分布而引起的虚假相关性，并证明我们的缓解方法在处理文本分类数据集中的标签分布偏差时具有优越性。