论文标题
变压器语言模型中的发展否定处理
Developmental Negation Processing in Transformer Language Models
论文作者
论文摘要
对于基于变压器的语言模型来说，使用否定的推理很难。虽然先前的研究使用心理语言学工具来探究变形金刚推理否定的能力，但没有人专注于发展心理学研究的否定类型。我们探讨了变形金刚通过将问题作为一种自然语言推理（NLI）任务构架来处理这种否定类别的能力。我们从流行的NLI数据集为我们的目标类别策划了一组诊断问题，并评估了一套模型的原因。我们发现，模型仅在某些类别上始终如一地表现更好，这表明其处理方式上有明确的区别。
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.
