Existing works have studied the impacts of the order of words within natural
text. They usually analyze it by destroying the original order of words to
create a scrambled sequence, and then comparing the models' performance between
the original and scrambled sequences. The experimental results demonstrate
marginal drops. Considering this findings, different hypothesis about word
order is proposed, including ``the order of words is redundant with lexical
semantics'', and ``models do not rely on word order''. In this paper, we
revisit the aforementioned hypotheses by adding a order reconstruction
perspective, and selecting datasets of different spectrum. Specifically, we
first select four different datasets, and then design order reconstruction and
continuing generation tasks. Empirical findings support that ChatGPT relies on
word order to infer, but cannot support or negate the redundancy relations
between word order lexical semantics.

在本文中，我们通过添加一种顺序重构的视角并选择不同范围的数据集，重新审视了关于词序的假设，包括 “词序与词汇语义冗余” 和 “模型不依赖词序”。实证结果支持 ChatGPT 在推断中依赖于词序，但不能支持或否定词序与词汇语义之间的冗余关系。