UniversalCEFR: Enabling Open Multilingual Research on Language Proficiency Assessment

Jun 2, 2025·
Joseph Marvin Imperial
,
Abdullah Barayan
,
Regina Stodden
,
Rodrigo Wilkens
,
Ricardo Munoz Sanchez
,
Lingyun Gao
,
Melissa Torgbi
,
Dawn Knight
,
Gail Forey
,
Reka R. Jablonkai
,
Ekaterina Kochmar
,
Robert Reynolds
,
Eugenio Ribeiro
,
Horacio Saggion
,
Elena Volodina
,
Sowmya Vajjala
,
Thomas Francois
Fernando Alva-Manchego
Fernando Alva-Manchego
,
Harish Tayyar Madabushi
· 0 min read
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.
Type
Publication
arXiv
Fernando Alva-Manchego
Authors
Lecturer
My research interests include text simplification, readability assessment, evaluation of natural language generation, and writing assistance.