@inproceedings{el-naggar-etal-2025-word,
    title = "Which Word Orders Facilitate Length Generalization in {LM}s? An Investigation with {GCG}-Based Artificial Languages",
    author = "El-Naggar, Nadine  and
      Kuribayashi, Tatsuki  and
      Briscoe, Ted",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1803/",
    pages = "35587--35601",
    ISBN = "979-8-89176-332-6",
    abstract = "Whether language models (LMs) have inductive biases that favor typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs) (White and Cotterell, 2021; Kuribayashi et al., 2024). In this paper, we extend these works from two perspectives. First, we extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) (Wood, 2014), which allows ALs to cover attested but previously overlooked constructions, such as unbounded dependency and mildly context-sensitive structures. Second, our evaluation focuses more on the generalization ability of LMs to process unseen longer test sentences. Thus, our ALs better capture features of natural languages and our experimental paradigm leads to clearer conclusions {---} typologically plausible word orders tend to be easier for LMs to productively generalize."
}