@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://aclanthology.org/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."
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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.</abstract>
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%0 Conference Proceedings
%T AILS-NTUA at SemEval-2025 Task 3: Leveraging Large Language Models and Translation Strategies for Multilingual Hallucination Detection
%A Karkani, Dimitra
%A Lymperaiou, Maria
%A Filandrianos, George
%A Spanos, Nikolaos
%A Voulodimos, Athanasios
%A Stamou, Giorgos
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F karkani-etal-2025-ails
%X 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.
%U https://aclanthology.org/2025.semeval-1.172/
%P 1289-1305
Markdown (Informal)
[AILS-NTUA at SemEval-2025 Task 3: Leveraging Large Language Models and Translation Strategies for Multilingual Hallucination Detection](https://aclanthology.org/2025.semeval-1.172/) (Karkani et al., SemEval 2025)
ACL