@inproceedings{abu-i-n-etal-2026-false,
    title = "False {F}riends or Cognates? A Cross-lingual Semantic Ambiguity Evaluation for {G}alician, {P}ortuguese and {S}panish",
    author = "Abu{\{}{\textbackslash}'{\textbackslash}i{\}}n, Marta V{\textbackslash}'azquez  and
      Camacho-Collados, Jose  and
      Garcia, Marcos",
    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.1818/",
    pages = "39199--39214",
    ISBN = "979-8-89176-390-6",
    abstract = "The linguistic proximity between Galician, Portuguese, and Spanish results in a lexical overlap that often conceals semantic interference. This is particularly evident in false friends, posing a challenge for NLP systems.In this work, we assess whether state-of-the-art language models can identify and process false friends among these languages. We introduce six cross-lingual datasets {--}created manually or using semi-automatic methods, with all instances being carefully verified{--} covering cognates and false friends. We evaluate a broad range of encoder and decoder models of varying sizes via zero-shot and few-shot settings. Our results highlight the challenging nature of the task, but also show the clear progress made by LLMs in recent years, particularly those of a larger size, with smaller language models struggling on the task. Notably, unlike other tasks where language distance poses additional challenges, we find that linguistic proximity itself introduces errors: closely related language pairs tend to perform worse, reflecting the challenge of semantic discrimination due to lexical overlap."
}