Argument Structure Constructions (ASCs) are one of the most well-studied construction groups, providing a unique opportunity to demonstrate the usefulness of Construction Grammar (CxG). For example, the caused-motion construction (CMC, ``She sneezed the foam off her cappuccino'') demonstrates that constructions must carry meaning, otherwise the fact that ``sneeze'' in this context causes movement cannot be explained. We form the hypothesis that this remains challenging even for state-of-the-art Large Language Models (LLMs), for which we devise a test based on substituting the verb with a prototypical motion verb. To be able to perform this test at statistically significant scale, in the absence of adequate CxG corpora, we develop a novel pipeline of NLP-assisted collection of linguistically annotated text. We show how dependency parsing and GPT-3.5 can be used to significantly reduce annotation cost and thus enable the annotation of rare phenomena at scale. We then evaluate GPT, Gemini, Llama2 and Mistral models for their understanding of the CMC using the newly collected corpus. We find that all models struggle with understanding the motion component that the CMC adds to a sentence.

翻译：论元结构构式（ASCs）是最深入研究的构式组之一，为证明构式语法（CxG）的实用性提供了独特契机。例如，致使运动构式（CMC，"她打了个喷嚏，把卡布奇诺上的泡沫喷掉了"）表明构式必须承载意义，否则无法解释"打喷嚏"在此语境中引发运动的逻辑。我们提出假设：即使对最先进的大语言模型（LLMs）而言，这一现象仍具挑战性。为此，我们设计了基于原型运动动词替换的测试方法。由于缺乏足够的CxG语料库，为在统计显著规模下实施该测试，我们开发了一种新型NLP辅助语言学标注文本采集流程。实验证明，借助依存句法分析和GPT-3.5可显著降低标注成本，进而实现罕见语言现象的大规模标注。随后利用新构建的语料库，评估了GPT、Gemini、Llama2和Mistral模型对致使运动构式的理解能力。结果表明，所有模型均难以理解CMC为句子添加的运动成分。