@inproceedings{shimada-etal-2025-hit,
    title = "{HIT}-{YOU} at {TSAR} 2025 Shared Task Leveraging Similarity-Based Few-Shot Prompting, Round-Trip Translation, and Self-Refinement for Readability-Controlled Text Simplification",
    author = "Shimada, Mao  and
      Bian, Kexin  and
      Ling, Zhidong  and
      Komachi, Mamoru",
    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-1.20/",
    pages = "231--241",
    ISBN = "979-8-89176-176-6",
    abstract = "We describe our submission to the TSAR 2025 shared task on readability-controlled text simplification, which evaluates systems on their ability to adjust linguistic complexity to specified CEFR levels while preserving meaning and coherence. We explored two complementary frameworks leveraging the shared task CEFR classifier as feedback. The first is an ensemble approach generating diverse candidates using multiple LLMs under zero-shot prompting with level-specific instructions and vocabulary lists, one-shot prompting, and round-trip translation. Candidates were filtered by predicted CEFR level before an LLM judge selected the final output. The second framework is a self-refinement loop, where a single candidate is iteratively revised with classifier feedback until matching the target level or reaching a maximum number of iterations. This study is among the first to apply round-trip translation and iterative self-refinement to controlled simplification, broadening the toolkit for adapting linguistic complexity."
}