@inproceedings{plum-etal-2026-ltzglue,
    title = "ltz{GLUE}: {L}uxembourgish General Language Understanding Evaluation",
    author = "Plum, Alistair  and
      K{\textbackslash}{''}orner, Felicia  and
      Lutgen, Anne-Marie  and
      Bernardy, Laura  and
      Philippy, Fred  and
      Milano, Emilia  and
      Rehlinger, Nils  and
      Lothritz, Cedric  and
      Ranasinghe, Tharindu  and
      Plank, Barbara  and
      Purschke, Christoph",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.476/",
    pages = "9791--9807",
    ISBN = "979-8-89176-395-1",
    abstract = "This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many european languages nowadays, LTZ is one of the official national languages that is often overlooked. We introduce new tasks and reuse existing ones to introduce the first official NLU benchmark and accompanying evaluation of encoder models for the language. Our tasks include common natural language processing tasks in binary and multi-class classification settings, including named entity recognition, topic classification, and intent classification. We evaluate various pre-trained language models for LTZ to present an overview of the current capabilities of these models on the LTZ language."
}