Recent advances in neural network language models have shown that it is
possible to derive expressive meaning representations by leveraging linguistic
associations in large-scale natural language data. These potentially Gestalt
representations have enabled state-of-the-art performance for many practical
applications. It would appear that we are on a pathway to empirically deriving
a robust and expressive computable semantics. A key question that arises is how
far can language data alone enable computers to understand the necessary truth
about the physical world? Attention to this question is warranted because our
future interactions with intelligent machines depends on how well our
techniques correctly represent and process the concepts (objects, properties,
and processes) that humans commonly observe to be true. After reviewing
existing protocols, the objective of this work is to explore this question
using a novel and tightly controlled reasoning test and to highlight what
models might learn directly from pure linguistic data.

这篇研究使用一个新颖的推理测试来探索语言数据对于计算机理解物理世界的可行性，并强调模型可能直接从纯语言数据中学习的内容。