@inproceedings{li-peng-2026-continued,
    title = "On the Continued Value of {U}niversal {D}ependencies in the Era of Large Language Models",
    author = "Li, Wenxi  and
      Peng, Jingyu",
    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.803/",
    pages = "17650--17662",
    ISBN = "979-8-89176-390-6",
    abstract = "The necessity of explicit linguistic representations has been increasingly questioned in the era of large language models (LLMs). In this work, we revisit this issue using Universal Dependencies (UD) as a case study, examining whether and in what ways this cross-lingual syntactic framework can still benefit contemporary LLMs. We focus on a cross-lingual adversarial paraphrase identification task that is designed to foreground the role of syntactic structure in semantic interpretation across languages. Within this setting, we systematically evaluate three strategies for integrating UD into LLMs: UD-Prompt, UD-Tuning, and UD-Attention. Our experiments show that, although the magnitude of gains depends on how UD-based structural priors interact with model behavior and cross-lingual variation, UD-augmented models consistently outperform their syntax-agnostic counterparts. Across strategies, we observe average accuracy improvements of 2.67{\%}, 8.24{\%}, and 2.53{\%}, respectively. These findings demonstrate that linguistic knowledge remains informative for LLMs, offering practical value in cross-lingual settings where structural alignment is challenging."
}