Reasoning using negation is known to be difficult for transformer-based language models. While previous studies have used the tools of psycholinguistics to probe a transformer's ability to reason over negation, none have focused on the types of negation studied in developmental psychology. We explore how well transformers can process such categories of negation, by framing the problem as a natural language inference (NLI) task. We curate a set of diagnostic questions for our target categories from popular NLI datasets and evaluate how well a suite of models reason over them. We find that models perform consistently better only on certain categories, suggesting clear distinctions in how they are processed.

翻译：使用否定的理由对于以变压器为基础的语言模型来说是难以理解的。虽然以前的研究利用了精神语言学工具来探究变压器的理性而不是否定的能力,但没有一项研究侧重于发展心理学中研究的否定类型。我们探讨变压器如何通过将问题描述为自然语言推论任务来处理这种否定类别。我们从流行的NLI数据集中为我们的目标类别设计了一系列诊断问题,并评估一套模型对这些类别的合理性。我们发现,模型的运行总是好于某些类别,表明在处理方法上的明确区别。