UniversalCEFR: Enabling Open Multilingual Research on Language Proficiency Assessment
This work addresses the need for standardized, accessible data in language proficiency research for the global research community, though it is incremental as it focuses on dataset creation and benchmarking rather than novel methods.
The authors tackled the problem of automated readability and language proficiency assessment by introducing UniversalCEFR, a large-scale multilingual dataset with 505,807 CEFR-labeled texts in 13 languages, and demonstrated its utility through benchmarking experiments that supported using linguistic features and fine-tuning pre-trained models.
We introduce UniversalCEFR, a large-scale multilingual and multidimensional dataset of texts annotated with CEFR (Common European Framework of Reference) levels in 13 languages. To enable open research in 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 modelling across tasks and languages. To demonstrate its utility, we conduct benchmarking 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 support using linguistic features and fine-tuning pretrained models in multilingual CEFR level assessment. Overall, UniversalCEFR aims to establish best practices in data distribution for language proficiency research by standardising dataset formats, and promoting their accessibility to the global research community.