A central question in natural language understanding (NLU) research is
whether high performance demonstrates the models' strong reasoning
capabilities. We present an extensive series of controlled experiments where
pre-trained language models are exposed to data that have undergone specific
corruption transformations. These involve removing instances of specific word
classes and often lead to non-sensical sentences. Our results show that
performance remains high on most GLUE tasks when the models are fine-tuned or
tested on corrupted data, suggesting that they leverage other cues for
prediction even in non-sensical contexts. Our proposed data transformations can
be used to assess the extent to which a specific dataset constitutes a proper
testbed for evaluating models' language understanding capabilities.

本论文针对自然语言理解研究的核心问题，以预训练语言模型为研究对象，通过对数据进行特定破坏转换使其变为无意义的句子，考察了在非意义上下文中该模型的判断能力，发现即便是在数据被破坏之后，该模型在大部分 GLUE 任务上的表现依旧保持较高水平，说明其在预测过程中利用了其他提示信息，同时引入了数据转换的概念，为评估模型语言理解能力提供了一种新方法。