@inproceedings{alharbi-etal-2026-islamiceval,
title = "{I}slamic{E}val 2026: The Second Shared Task of Capturing {LLM}s Hallucination in Islamic Content",
author = "Alharbi, Rahaf and
Alturki, Abdulelah and
Mansour, Watheq and
Malhas, Rana and
Mubarak, Hamdy and
Darwish, Kareem Mohamed and
Elsayed, Tamer and
Magdy, Walid",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.125/",
pages = "1022--1034",
ISBN = "979-8-89176-497-2",
abstract = "We describe the main findings of the second IslamicEval shared task on hallucination detection in LLM-generated Islam related text (IslamicEval 2026). While the first occurrence of the task in 2025 separately evaluated hallucination detection/correction in quoted Quranic verses and Hadiths alongside a distinct QA retrieval subtask, this year integrates these objectives into four fine-grained subtasks with a much larger provided training set. Given an Islamic question and an LLM response, systems must: 1) detect all cited Quran and Hadith references (including verse numbers and narrators); 2) identify hallucinated citations; 3) correct the hallucinated content; and 4) assess the relevance of every citation to the prompt. Sixteen teams participated in the final phase across the four subtasks, utilizing diverse pipelines that highlight both the challenge of the task and the necessity of mitigation strategies in Islamic AI applications."
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<abstract>We describe the main findings of the second IslamicEval shared task on hallucination detection in LLM-generated Islam related text (IslamicEval 2026). While the first occurrence of the task in 2025 separately evaluated hallucination detection/correction in quoted Quranic verses and Hadiths alongside a distinct QA retrieval subtask, this year integrates these objectives into four fine-grained subtasks with a much larger provided training set. Given an Islamic question and an LLM response, systems must: 1) detect all cited Quran and Hadith references (including verse numbers and narrators); 2) identify hallucinated citations; 3) correct the hallucinated content; and 4) assess the relevance of every citation to the prompt. Sixteen teams participated in the final phase across the four subtasks, utilizing diverse pipelines that highlight both the challenge of the task and the necessity of mitigation strategies in Islamic AI applications.</abstract>
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%0 Conference Proceedings
%T IslamicEval 2026: The Second Shared Task of Capturing LLMs Hallucination in Islamic Content
%A Alharbi, Rahaf
%A Alturki, Abdulelah
%A Mansour, Watheq
%A Malhas, Rana
%A Mubarak, Hamdy
%A Darwish, Kareem Mohamed
%A Elsayed, Tamer
%A Magdy, Walid
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F alharbi-etal-2026-islamiceval
%X We describe the main findings of the second IslamicEval shared task on hallucination detection in LLM-generated Islam related text (IslamicEval 2026). While the first occurrence of the task in 2025 separately evaluated hallucination detection/correction in quoted Quranic verses and Hadiths alongside a distinct QA retrieval subtask, this year integrates these objectives into four fine-grained subtasks with a much larger provided training set. Given an Islamic question and an LLM response, systems must: 1) detect all cited Quran and Hadith references (including verse numbers and narrators); 2) identify hallucinated citations; 3) correct the hallucinated content; and 4) assess the relevance of every citation to the prompt. Sixteen teams participated in the final phase across the four subtasks, utilizing diverse pipelines that highlight both the challenge of the task and the necessity of mitigation strategies in Islamic AI applications.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.125/
%P 1022-1034
Markdown (Informal)
[IslamicEval 2026: The Second Shared Task of Capturing LLMs Hallucination in Islamic Content](https://aclanthology.org/2026.arabicnlp-sharedtasks.125/) (Alharbi et al., ArabicNLP 2026)
ACL
- Rahaf Alharbi, Abdulelah Alturki, Watheq Mansour, Rana Malhas, Hamdy Mubarak, Kareem Mohamed Darwish, Tamer Elsayed, and Walid Magdy. 2026. IslamicEval 2026: The Second Shared Task of Capturing LLMs Hallucination in Islamic Content. In Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks, pages 1022–1034, Budapest, Hungary. Association for Computational Linguistics.