@inproceedings{barbu-etal-2025-easyjon,
    title = "{E}asy{J}on at {TSAR} 2025 Shared Task Evaluation of Automated Text Simplification with {LLM}-as-a-Judge",
    author = "Barbu, Paul-Gerhard  and
      Lipska-Dieck, Adrianna  and
      Lindner, Lena",
    editor = "Shardlow, Matthew  and
      Alva-Manchego, Fernando  and
      North, Kai  and
      Stodden, Regina  and
      Saggion, Horacio  and
      Khallaf, Nouran  and
      Hayakawa, Akio",
    booktitle = "Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.tsar-1.14/",
    pages = "173--182",
    ISBN = "979-8-89176-176-6",
    abstract = "This paper presents an approach to automated text simplification for CEFR A2 and B1 levels using large language models and prompt engineering. We evaluate seven models across three prompting strategies short, descriptive, and descriptive with examples. A two-round evaluation system using LLM-as-a-Judge and traditional metrics for text simplification determines optimal model-prompt combinations for final submissions. Results demonstrate that descriptive prompts consistently outperform other strategies across all models, achieving 46-65{\%} of first-place rankings. Qwen3 shows superior performance for A2-level simplification, while B1-level results are more balanced across models. The LLM-as-a-Judge evaluation method shows strong alignment with traditional metrics while providing enhanced explainability."
}