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arXiv · 2503.02442

AILS-NTUA at SemEval-2025 Task 3: Leveraging Large Language Models and Translation Strategies for Multilingual Hallucination Detection

Dimitra Karkani·Maria Lymperaiou·Giorgos Filandrianos·Nikolaos Spanos·Athanasios Voulodimos·Giorgos Stamou
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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.

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Dimitra Karkani, Maria Lymperaiou, Giorgos Filandrianos, Nikolaos Spanos, Athanasios Voulodimos, Giorgos Stamou. 2025-03-04. AILS-NTUA at SemEval-2025 Task 3: Leveraging Large Language Models and Translation Strategies for Multilingual Hallucination Detection. https://arxiv.org/abs/2503.02442

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