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.

本文对深度学习在自然语言处理（NLP）方面的最新应用进行了调查研究，发现 transformer-based language models 在检测受控自然语言片段的有效推理方面表现出明显的过拟合问题，未能获取主导推理的逻辑原则。

Transformer 能否在自然语言的片段中进行推理？

Can Transformers Reason in Fragments of Natural Language?

We propose analyzing conditional reasoning by appeal to a notion of
intervention on a simulation program, formalizing and subsuming a number of
approaches to conditional thinking in the recent AI literature. Our main
results include a series of axiomatizations, allowing comparison between this
framework and existing frameworks (normality-ordering models, causal structural
equation models), and a complexity result establishing NP-completeness of the
satisfiability problem. Perhaps surprisingly, some of the basic logical
principles common to all existing approaches are invalidated in our causal
simulation approach. We suggest that this additional flexibility is important
in modeling some intuitive examples.

本文通过介绍一个基于干预模拟程序的条件推理方法来形式化和涵盖 AI 领域内一些条件思维方法，提出了若干公理化结果，并确立了该满足性问题的 NP 完备性，与现有框架（正常性排序模型，因果结构方程模型）进行比较。 一些基本的逻辑原理在我们的因果模拟方法中被证明是无效的。 尽管如此，作者认为该方法对于建模某些直观的例子是很重要的。