The goal of this study is to investigate whether a Transformer-based neural
language model infers lexical semantics and use this information for the
completion of morphosyntactic patterns. The semantic properties considered are
telicity (also combined with definiteness) and agentivity. Both act at the
interface between semantics and morphosyntax: they are semantically determined
and syntactically encoded. The tasks were submitted to both the computational
model and a group of Italian native speakers. The comparison between the two
groups of data allows us to investigate to what extent neural language models
capture significant aspects of human semantic competence.

研究目的是调查基于 Transformer 的神经语言模型是否能推断词汇语义，并利用这些信息来完成形态句法模式的构建；研究考虑了 telicity（兼具确定性）和 agentivity 两个语义特性，它们在语义和句法语言之间的交接处起作用，既受语义决定，又在句法上编码；实验分别让计算模型和一群意大利母语者完成了这些任务，通过比较两组数据，我们可以研究神经语言模型在多大程度上捕捉到人类语义能力的重要方面。

GilBERTo 中的能动性和目的性：认知启示

Agentività e telicità in GilBERTo: implicazioni cognitive

Recent advances in large language models have prompted researchers to examine
their abilities across a variety of linguistic tasks, but little has been done
to investigate how models handle the interactions in meaning across words and
larger syntactic forms -- i.e. phenomena at the intersection of syntax and
semantics. We present the semantic notion of agentivity as a case study for
probing such interactions. We created a novel evaluation dataset by utilitizing
the unique linguistic properties of a subset of optionally transitive English
verbs. This dataset was used to prompt varying sizes of three model classes to
see if they are sensitive to agentivity at the lexical level, and if they can
appropriately employ these word-level priors given a specific syntactic
context. Overall, GPT-3 text-davinci-003 performs extremely well across all
experiments, outperforming all other models tested by far. In fact, the results
are even better correlated with human judgements than both syntactic and
semantic corpus statistics. This suggests that LMs may potentially serve as
more useful tools for linguistic annotation, theory testing, and discovery than
select corpora for certain tasks.

以 semantic notion of agentivity 为例，研究了 large language models 在句法和语义交互方面的表现；作者创建了一个新颖的评估数据集，发现 GPT-3 的表现非常好，甚至比语法和语义语料库的统计结果还要好，这表明大语言模型可能对语言注释、理论测试和发现比某些任务的语料库更有用。