Hybrid Human-LLM Corpus Construction and LLM Evaluation for the Caused-Motion Construction

Abstract

The caused-motion construction (CMC, “She sneezed the foam off her cappuccino”) is one of the most well-studied constructions in Construction Grammar (CxG). It is a prime example for describing how constructions must carry meaning, as otherwise the fact that “sneeze” in this context takes two arguments and causes motion 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 a 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 LLMs can be used to significantly reduce annotation cost and thus enable the annotation of rare phenomena at scale. We then evaluate OpenAI, Gemma3, Llama3, OLMo2, Mistral and Aya models for their understanding of the CMC using the newly collected corpus. We find that most models struggle with understanding the motion component that the CMC adds to a sentence.