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

2025/03/04 by Dimitra Karkani, Maria Lymperaiou, Karkani, Dimitra +9
Psychology · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Mental Health via Writing #Misinformation and Its Impacts

paper · pdf · doi:10.48550/arxiv.2503.02442

openalex publication_date 2025/03/04 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

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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