Filtering by topic arabic(1)Clear all filters

• Paper·arXiv — Language & NLP (cs.CL)·20 Jun 2026

  Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generation

  Nour Rabih, Chatrine Qwaider, Ted Briscoe

  A multi-dimensional evaluation framework was proposed to assess LLMs' ability to control readability levels in Arabic text generation for CEFR levels. Structured prompting with lexical constraints significantly improved alignment, achieving high cosine similarity and near-perfect agreement with predicted readability levels.

  Original abstract

  While Large Language Models (LLMs) can generate fluent Arabic text, their ability to reliably control readability levels remains unclear. We propose a multi-dimensional evaluation framework for Common European Framework of Reference for Language (CEFR)-controlled Arabic text generation, assessing whether instruction-following LLMs can serve as reliable generators for adaptive language learning. Our framework integrates controlled prompting, automatic readability prediction using a validated Taha-19 model, lexical constraint validation, and syntactic complexity profiling. Results show that structured prompting substantially improves CEFR alignment. In particular, CEFR-guided prompting with lexical constraints achieves the highest conformity to reference linguistic profiles (0.91 cosine similarity) and near-perfect agreement with predicted readability levels (0.99), while unconstrained prompting exhibits weak control. These findings establish an empirical foundation for integrating readability-aware Arabic text generation into adaptive educational systems.

  readability-assessmentllmcefrarabicadaptive-education★ 9/10