@inproceedings{karkani-etal-2025-ails,
    title = "{AILS}-{NTUA} at {S}em{E}val-2025 Task 3: Leveraging Large Language Models and Translation Strategies for Multilingual Hallucination Detection",
    author = "Karkani, Dimitra  and
      Lymperaiou, Maria  and
      Filandrianos, George  and
      Spanos, Nikolaos  and
      Voulodimos, Athanasios  and
      Stamou, Giorgos",
    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/transition-to-people-yaml/2025.semeval-1.172/",
    pages = "1289--1305",
    ISBN = "979-8-89176-273-2",
    abstract = "Multilingual hallucination detection stands as an underexplored challenge, which the Mu-SHROOM shared task seeks to address. In this work, we propose an efficient, training-free LLM prompting strategy that enhances detection by translating multilingual text spans into English. Our approach achieves competitive rankings across multiple languages, securing two first positions in low-resource languages. The consistency of our results highlights the effectiveness of our translation strategy for hallucination detection, demonstrating its applicability regardless of the source language."
}