@inproceedings{im-lee-2024-gpt,
    title = "What {GPT}-4 Knows about Aspectual Coercion: Focused on {``}Begin the Book{''}",
    author = "Im, Seohyun  and
      Lee, Chungmin",
    editor = "Zock, Michael  and
      Chersoni, Emmanuele  and
      Hsu, Yu-Yin  and
      de Deyne, Simon",
    booktitle = "Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cogalex-1.7",
    pages = "56--67",
    abstract = "This paper explores whether Pre-trained Large Language Models (PLLMs) like GPT-4 can grasp profound linguistic insights into language phenomena such as Aspectual Coercion through interaction with Microsoft{'}s Copilot, which integrates GPT-4. Firstly, we examined Copilot{'}s understanding of the co-occurrence constraints of the aspectual verb {``}begin{''} and the complex-type noun {``}book{''} using the classic illustration of Aspectual Coercion, {``}begin the book.{''} Secondly, we verified Copilot{'}s awareness of both the default interpretation of {``}begin the book{''} with no specific context and the contextually preferred interpretation. Ultimately, Copilot provided appropriate responses regarding potential interpretations of {``}begin the book{''} based on its distributional properties and context-dependent preferred interpretations. However, it did not furnish sophisticated explanations concerning these interpretations from a linguistic theoretical perspective. On the other hand, by offering diverse interpretations grounded in distributional properties, language models like GPT-4 demonstrated their potential contribution to the refinement of linguistic theories. Furthermore, we suggested the feasibility of employing Language Models to construct language resources associated with language phenomena including Aspectual Coercion.",
}

This paper explores whether Pre-trained Large Language Models (PLLMs) like GPT-4 can grasp profound linguistic insights into language phenomena such as Aspectual Coercion through interaction with Microsoft{'}s Copilot, which integrates GPT-4. Firstly, we examined Copilot{'}s understanding of the co-occurrence constraints of the aspectual verb {``}begin{''} and the complex-type noun {``}book{''} using the classic illustration of Aspectual Coercion, {``}begin the book.{''} Secondly, we verified Copilot{'}s awareness of both the default interpretation of {``}begin the book{''} with no specific context and the contextually preferred interpretation. Ultimately, Copilot provided appropriate responses regarding potential interpretations of {``}begin the book{''} based on its distributional properties and context-dependent preferred interpretations. However, it did not furnish sophisticated explanations concerning these interpretations from a linguistic theoretical perspective. On the other hand, by offering diverse interpretations grounded in distributional properties, language models like GPT-4 demonstrated their potential contribution to the refinement of linguistic theories. Furthermore, we suggested the feasibility of employing Language Models to construct language resources associated with language phenomena including Aspectual Coercion.
Im, Seohyun Lee, Chungmin
What GPT-4 Knows about Aspectual Coercion: Focused on “Begin the Book”