@inproceedings{kryvosheieva-etal-2026-different,
    title = "Different types of syntactic agreement recruit the same units within large language models",
    author = "Kryvosheieva, Daria  and
      de Varda, Andrea Gregor  and
      Fedorenko, Evelina  and
      Tuckute, Greta",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.7/",
    pages = "209--227",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the model remains an open question. We investigate whether different syntactic phenomena recruit shared or distinct components in LLMs. Using a functional localization approach inspired by cognitive neuroscience, we identify the LLM units most responsive to 67 English syntactic phenomena in seven open-weight models. These units are consistently recruited across sentence instances and causally support model performance. Critically, different types of syntactic agreement (e.g., subject-verb, anaphor, determiner-noun) recruit overlapping sets of units, suggesting that agreement constitutes a meaningful functional category in LLMs. This pattern holds in Russian and Chinese, and further, across languages: in a cross-lingual analysis of 57 languages, syntactically similar languages share more units for subject-verb agreement. Taken together, these findings reveal that syntactic agreement{---}a critical marker of syntactic dependencies{---}constitutes a meaningful category within LLMs' representational spaces."
}