Machine learning systems are known to be sensitive to spurious correlations
between biased features of the inputs (e.g., background, texture, and secondary
objects) and the corresponding labels. These features and their correlations
with the labels are known as "spurious" because they tend to change with shifts
in real-world data distributions, which can negatively impact the model's
generalization and robustness. In this survey, we provide a comprehensive
review of this issue, along with a taxonomy of current state-of-the-art methods
for addressing spurious correlations in machine learning models. Additionally,
we summarize existing datasets, benchmarks, and metrics to aid future research.
The paper concludes with a discussion of the recent advancements and future
research challenges in this field, aiming to provide valuable insights for
researchers in the related domains.

机器学习系统对输入的偏倚特征（例如背景、纹理和次要对象）与相应标签之间的虚假相关性非常敏感。本综述提供了对该问题的全面回顾，以及现有最先进方法的分类体系，用于解决机器学习模型中的虚假相关性。此外，我们总结了现有的数据集、标杆和度量方法，以帮助未来的研究。综述最后讨论了该领域的最新进展和未来的研究挑战，旨在为相关领域的研究人员提供有价值的见解。

机器学习中的虚假相关性：一项调查

Spurious Correlations in Machine Learning: A Survey

As commonly-used methods for debiasing natural language understanding (NLU)
models, dataset refinement approaches heavily rely on manual data analysis, and
thus maybe unable to cover all the potential biased features. In this paper, we
propose IBADR, an Iterative Bias-Aware Dataset Refinement framework, which
debiases NLU models without predefining biased features. We maintain an
iteratively expanded sample pool. Specifically, at each iteration, we first
train a shallow model to quantify the bias degree of samples in the pool. Then,
we pair each sample with a bias indicator representing its bias degree, and use
these extended samples to train a sample generator. In this way, this generator
can effectively learn the correspondence relationship between bias indicators
and samples. Furthermore, we employ the generator to produce pseudo samples
with fewer biased features by feeding specific bias indicators. Finally, we
incorporate the generated pseudo samples into the pool. Experimental results
and in-depth analyses on two NLU tasks show that IBADR not only significantly
outperforms existing dataset refinement approaches, achieving SOTA, but also is
compatible with model-centric methods.

本研究提出了 IBADR，一种迭代的偏差感知数据集优化框架，它在不预定义偏差特征的情况下对 NLU 模型进行去偏。实验结果和深入分析表明，IBADR 不仅显著优于现有的数据集优化方法，达到 SOTA，还与以模型为中心的方法兼容。