论文标题
变形金刚可以在自然语言的碎片中推理吗？
Can Transformers Reason in Fragments of Natural Language?
论文作者
论文摘要
最先进的基于深度学习的自然语言处理方法（NLP）具有各种能力，涉及自然语言文本的推理。在本文中，我们进行了一项大规模的实证研究，研究了自然语言受控片段正式有效推断的检测，满足性问题变得越来越复杂。我们发现，尽管基于变压器的语言模型在这些情况下表现出色，但更深入的分析将它们似乎过于效果到数据中的表面模式，而不是获取有关这些片段中推理的逻辑原理。
State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex. We find that, while transformer-based language models perform surprisingly well in these scenarios, a deeper analysis re-veals that they appear to overfit to superficial patterns in the data rather than acquiring the logical principles governing the reasoning in these fragments.
