@inproceedings{imperial-etal-2025-universalcefr,
    title = "{U}niversal{CEFR}: Enabling Open Multilingual Research on Language Proficiency Assessment",
    author = "Imperial, Joseph Marvin  and
      Barayan, Abdullah  and
      Stodden, Regina  and
      Wilkens, Rodrigo  and
      Mu{\~n}oz S{\'a}nchez, Ricardo  and
      Gao, Lingyun  and
      Torgbi, Melissa  and
      Knight, Dawn  and
      Forey, Gail  and
      Jablonkai, Reka R.  and
      Kochmar, Ekaterina  and
      Reynolds, Robert Joshua  and
      Ribeiro, Eug{\'e}nio  and
      Saggion, Horacio  and
      Volodina, Elena  and
      Vajjala, Sowmya  and
      Fran{\c{c}}ois, Thomas  and
      Alva-Manchego, Fernando  and
      Tayyar Madabushi, Harish",
    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.491/",
    pages = "9714--9766",
    ISBN = "979-8-89176-332-6",
    abstract = "We introduce UniversalCEFR, a large-scale multilingual multidimensional dataset of texts annotated according to the CEFR (Common European Framework of Reference) scale in 13 languages. To enable open research in both automated readability and language proficiency assessment, UniversalCEFR comprises 505,807 CEFR-labeled texts curated from educational and learner-oriented resources, standardized into a unified data format to support consistent processing, analysis, and modeling across tasks and languages. To demonstrate its utility, we conduct benchmark experiments using three modelling paradigms: a) linguistic feature-based classification, b) fine-tuning pre-trained LLMs, and c) descriptor-based prompting of instruction-tuned LLMs. Our results further support using linguistic features and fine-tuning pretrained models in multilingual CEFR level assessment. Overall, UniversalCEFR aims to establish best practices in data distribution in language proficiency research by standardising dataset formats and promoting their accessibility to the global research community."
}