We present an approach for assessing how multilingual large language models
(LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to
recover constituent and dependency structures by casting parsing as sequence
labeling. To do so, we select a few LLMs and study them on 13 diverse UD
treebanks for dependency parsing and 10 treebanks for constituent parsing. Our
results show that: (i) the framework is consistent across encodings, (ii)
pre-trained word vectors do not favor constituency representations of syntax
over dependencies, (iii) sub-word tokenization is needed to represent syntax,
in contrast to character-based models, and (iv) occurrence of a language in the
pretraining data is more important than the amount of task data when recovering
syntax from the word vectors.

我们提出了一种评估多语言大型语言模型在多形式语法结构方面学习句法的方法，将分析转化为序列标记，通过选择几个语言模型并在 13 个多样化的依赖解析树库和 10 个成分解析树库上研究它们，结果表明：（i）该框架在多种编码上一致，（ii）预先训练的词向量不偏好成分句法表示，而是倾向于依赖表示，（iii）子词标记化需要用于表示语法，与基于字符的模型不同，（iv）从词向量中恢复语法时，语言在预训练数据中的出现比任务数据的数量更重要。