The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision. However, it remains unclear how these models learn to generalise across languages. In this work, we conjecture that multilingual pre-trained models can derive language-universal abstractions about grammar. In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages. We conduct the first large-scale empirical study over 43 languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe. Our findings show that the cross-lingual overlap between neurons is significant, but its extent may vary across categories and depends on language proximity and pre-training data size.

该研究采用神经元级别探测器，在包括43种语言的14个形态句法类别上进行了大规模的实证研究，并发现了多语言预训练模型的跨语言重叠是显著的，但其范围可能因语言接近性和预培训数据的大小而异，支持其能够对语法进行语言通用的抽象。

同一神经元，不同语言：探究多语言预训练模型中的形态语法