@inproceedings{tayyar-madabushi-bonial-2025-construction,
    title = "Construction Grammar Evidence for How {LLM}s Use Context-Directed Extrapolation to Solve Tasks",
    author = "Tayyar Madabushi, Harish  and
      Bonial, Claire",
    editor = "Bonial, Claire  and
      Torgbi, Melissa  and
      Weissweiler, Leonie  and
      Blodgett, Austin  and
      Beuls, Katrien  and
      Van Eecke, Paul  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Second International Workshop on Construction Grammars and NLP",
    month = sep,
    year = "2025",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/2025.cxgsnlp-1.20/",
    pages = "190--201",
    ISBN = "979-8-89176-318-0",
    abstract = "In this paper, we apply the lens of Construction Grammar to provide linguistically-grounded evidence for the recently introduced view of LLMs that moves beyond the ``stochastic parrot'' and ``emergent Artificial General Intelligence'' extremes. We provide further evidence, this time rooted in linguistic theory, that the capabilities of LLMs are best explained by a process of context-directed extrapolation from their training priors. This mechanism, guided by in-context examples in base models or the prompt in instruction-tuned models, clarifies how LLM performance can exceed stochastic parroting without achieving the scalable, general-purpose reasoning seen in humans. Construction Grammar is uniquely suited to this investigation, as it provides a precise framework for testing the boundary between true generalization and sophisticated pattern-matching on novel linguistic tasks. The ramifications of this framework explaining LLM performance are three-fold: first, there is explanatory power providing insights into seemingly idiosyncratic LLM weaknesses and strengths; second, there are empowering methods for LLM users to improve performance of smaller models in post-training; third, there is a need to shift LLM evaluation paradigms so that LLMs are assessed relative to the prevalence of relevant priors in training data, and Construction Grammar provides a framework to create such evaluation data."
}