While the Large Language Models (LLMs) dominate a majority of language understanding tasks, previous work shows that some of these results are supported by modelling spurious correlations of training datasets. Authors commonly assess model robustness by evaluating their models on out-of-distribution (OOD) datasets of the same task, but these datasets might share the bias of the training dataset. We propose a simple method for measuring a scale of models' reliance on any identified spurious feature and assess the robustness towards a large set of known and newly found prediction biases for various pre-trained models and debiasing methods in Question Answering (QA). We find that while existing debiasing methods can mitigate reliance on a chosen spurious feature, the OOD performance gains of these methods can not be explained by mitigated reliance on biased features, suggesting that biases are shared among different QA datasets. Finally, we evidence this to be the case by measuring that the performance of models trained on different QA datasets relies comparably on the same bias features. We hope these results will motivate future work to refine the reports of LMs' robustness to a level of adversarial samples addressing specific spurious features.

翻译：尽管大语言模型（LLMs）在大多数语言理解任务中占据主导地位，但先前研究表明，其中部分成果源于对训练数据集中虚假相关性的建模。研究者通常通过在同一任务中的分布外数据集上评估模型来检验其鲁棒性，但这些数据集可能与训练数据集共享偏差。我们提出了一种简单方法，用于衡量模型对任何已识别虚假特征的依赖程度，并评估各种预训练模型和去偏方法在问答任务中针对大量已知及新发现的预测偏差的鲁棒性。我们发现，现有去偏方法虽能减轻模型对特定虚假特征的依赖，但这些方法在分布外数据集上的性能提升并不能用偏倚特征依赖程度的降低来解释，这表明不同问答数据集之间存在共享偏差。最终，我们通过测量证明了这一结论：在不同问答数据集上训练的模型对相同偏倚特征的依赖程度具有可比性。我们希望这些结果能推动未来工作将语言模型鲁棒性的评估细化到针对特定虚假特征的对抗样本层面。