@inproceedings{alva-manchego-etal-2025-findings,
    title = "Findings of the {TSAR} 2025 Shared Task on Readability-Controlled Text Simplification",
    author = "Alva-Manchego, Fernando  and
      Stodden, Regina  and
      Imperial, Joseph Marvin  and
      Barayan, Abdullah  and
      North, Kai  and
      Tayyar Madabushi, Harish",
    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-1.8/",
    pages = "116--130",
    ISBN = "979-8-89176-176-6",
    abstract = "This paper presents the findings of the first Shared Task on Readability-Controlled Text Simplification at TSAR 2025. The task required systems to simplify English texts to specific target readability levels of the Common European Framework of Reference for Languages (CEFR). We received 48 submissions from 20 participating teams, with approaches predominantly based on large language models (LLMs), which included iterative refinement, multi-agent setups, and LLM-as-a-judge pipelines. For this shared task, we developed a new dataset of pedagogical texts and evaluated submissions using a weighted combination of semantic similarity and CEFR-level accuracy. The results of the participating teams demonstrate that while LLMs can perform substantially well on this task, dependable and controlled simplification often requires complex, multi-iterative processes. Our findings also suggest that the capabilities of current systems are beginning to saturate existing automatic evaluation metrics, underscoring the need for reevaluation and practicality."
}