论文标题
数据腐败如何影响自然语言理解模型？胶水数据集的研究
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets
论文作者
论文摘要
自然语言理解（NLU）研究中的一个核心问题是，高性能是否证明了模型的强大推理能力。我们提供了一系列广泛的受控实验，其中预先训练的语言模型暴露于经历了特定损坏转换的数据。这些涉及删除特定单词类的实例，并经常导致非敏感句子。我们的结果表明，当模型对损坏的数据进行微调或测试时，大多数胶水任务的性能仍然很高，这表明即使在非敏感情况下，它们也将其他提示用于预测。我们提出的数据转换可用于评估特定数据集构成适当测试台的程度，用于评估模型的语言理解能力。
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.
