@inproceedings{kennedy-2025-evidence,
    title = "Evidence of Generative Syntax in {LLM}s",
    author = "Kennedy, Mary",
    editor = "Boleda, Gemma  and
      Roth, Michael",
    booktitle = "Proceedings of the 29th Conference on Computational Natural Language Learning",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.conll-1.25/",
    doi = "10.18653/v1/2025.conll-1.25",
    pages = "377--396",
    ISBN = "979-8-89176-271-8",
    abstract = "The syntactic probing literature has been largely limited to shallow structures like dependency trees, which are unable to capture the subtle differences in sub-surface syntactic structures that yield semantic nuances. These structures are captured by theories of syntax like generative syntax, but have not been researched in the LLM literature due to the difficulties in probing these complex structures with many silent, covert nodes. Our work presents a method for overcoming this limitation by deploying Hewitt and Manning{'}s (2019) dependency-trained probe on sentence constructions whose structural representation is identical in a dependency parse, but differs in theoretical syntax. If a pretrained language model has captured the theoretical syntax structure, then the probe{'}s predicted distances should vary in syntactically-predicted ways. Using this methodology and a novel dataset, we find evidence that LLMs have captured syntactic structures far richer than previously realized, indicating LLMs are able to capture the nuanced meanings that result from sub-surface differences in structural form."
}