@inproceedings{lee-etal-2025-chill,
    title = "{CHILL} at {S}em{E}val-2025 Task 2: You Can{'}t Just Throw Entities and {H}ope{---}{M}ake Your {LLM} to Get Them Right",
    author = "Lee, Jaebok  and
      Ryu, Yonghyun  and
      Park, Seongmin  and
      Choi, Yoonjung",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.75/",
    pages = "539--545",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we describe our approach for the SemEval 2025 Task 2 on Entity-Aware Machine Translation (EA-MT).Our system aims to improve the accuracy of translating named entities by combining two key approaches: Retrieval Augmented Generation (RAG) and iterative self-refinement techniques using Large Language Models (LLMs).A distinctive feature of our system is its self-evaluation mechanism, where the LLM assesses its own translations based on two key criteria: the accuracy of entity translations and overall translation quality. We demonstrate how these methods work together and effectively improve entity handling while maintaining high-quality translations."
}