@inproceedings{oviedo-etal-2025-taskgen,
    title = "task{G}en at {TSAR} 2025 Shared Task Exploring prompt strategies with linguistic knowledge",
    author = "Oviedo, Juan Cruz  and
      Comelles Pujadas, Elisabet  and
      Alonso Alemany, Laura  and
      Atserias Batalla, Jordi",
    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-1.13/",
    pages = "160--172",
    ISBN = "979-8-89176-176-6",
    abstract = "TaskGen ranked as 6th best team in the TSAR 2025 shared task for English text adaptation to a target CEFR level. Our experiments consisted of prompting a Llama-3.1-8B-Instruct model with linguistic descriptors of the target level, examples of adaptations and multi-step approaches. Our best run, 13th in the overall ranking, applied an ensemble strategy using a voting mechanism to find the most adequate among 10 texts, each produced by a different prompting strategy."
}