@inproceedings{zamaraeva-etal-2025-comparing,
    title = "Comparing {LLM}-generated and human-authored news text using formal syntactic theory",
    author = "Zamaraeva, Olga  and
      Flickinger, Dan  and
      Bond, Francis  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.443/",
    pages = "9041--9060",
    ISBN = "979-8-89176-251-0",
    abstract = "This study provides the first comprehensive comparison of New York Times-style text generated by six large language models against real, human-authored NYT writing. The comparison is based on a formal syntactic theory. We use Head-driven Phrase Structure Grammar (HPSG) to analyze the grammatical structure of the texts. We then investigate and illustrate the differences in the distributions of HPSG grammar types, revealing systematic distinctions between human and LLM-generated writing. These findings contribute to a deeper understanding of the syntactic behavior of LLMs as well as humans, within the NYT genre."
}