@inproceedings{alsuhaibani-alkaoud-2026-yaqeen,
title = "Yaqeen at {I}slamic{E}val 2026: From Contextual Detection to Retrieval-Based Verification and Canonical Correction of {LLM}-Generated {A}rabic Islamic Citations",
author = "Alsuhaibani, Shahad and
Alkaoud, Mohamed",
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.129/",
pages = "1050--1054",
ISBN = "979-8-89176-497-2",
abstract = "Large language models are increasingly used to answer questions about religious texts, yet their fluent responses may contain misquoted, misattributed, or fabricated citations. Detecting such hallucinations requires identifying citation components, verifying their correctness, and recovering the canonical text when a quotation is incorrect. We present Yaqeen{'}s submission to the IslamicEval 2026 shared task, which evaluates these capabilities through three subtasks: detection of Qur{'}anic verses, Hadith text, chains of narration, and claimed sources; identification of incorrect citation fragments; and correction of erroneous Qur{'}anic and Hadith quotations. We develop task-specific systems combining AraBERT-based character-level sequence labeling with retrieval-based matching, classification, and reranking. Our systems achieve 96.78{\%} character-level Macro-F1 on Task 1, 91.07{\%} accuracy on Task 2, and 78.02{\%} accuracy on Task 3. The results show that combining contextual Arabic representations with retrieval-based evidence is effective for fine-grained hallucination detection and correction in Arabic Islamic content."
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<abstract>Large language models are increasingly used to answer questions about religious texts, yet their fluent responses may contain misquoted, misattributed, or fabricated citations. Detecting such hallucinations requires identifying citation components, verifying their correctness, and recovering the canonical text when a quotation is incorrect. We present Yaqeen’s submission to the IslamicEval 2026 shared task, which evaluates these capabilities through three subtasks: detection of Qur’anic verses, Hadith text, chains of narration, and claimed sources; identification of incorrect citation fragments; and correction of erroneous Qur’anic and Hadith quotations. We develop task-specific systems combining AraBERT-based character-level sequence labeling with retrieval-based matching, classification, and reranking. Our systems achieve 96.78% character-level Macro-F1 on Task 1, 91.07% accuracy on Task 2, and 78.02% accuracy on Task 3. The results show that combining contextual Arabic representations with retrieval-based evidence is effective for fine-grained hallucination detection and correction in Arabic Islamic content.</abstract>
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%0 Conference Proceedings
%T Yaqeen at IslamicEval 2026: From Contextual Detection to Retrieval-Based Verification and Canonical Correction of LLM-Generated Arabic Islamic Citations
%A Alsuhaibani, Shahad
%A Alkaoud, Mohamed
%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 alsuhaibani-alkaoud-2026-yaqeen
%X Large language models are increasingly used to answer questions about religious texts, yet their fluent responses may contain misquoted, misattributed, or fabricated citations. Detecting such hallucinations requires identifying citation components, verifying their correctness, and recovering the canonical text when a quotation is incorrect. We present Yaqeen’s submission to the IslamicEval 2026 shared task, which evaluates these capabilities through three subtasks: detection of Qur’anic verses, Hadith text, chains of narration, and claimed sources; identification of incorrect citation fragments; and correction of erroneous Qur’anic and Hadith quotations. We develop task-specific systems combining AraBERT-based character-level sequence labeling with retrieval-based matching, classification, and reranking. Our systems achieve 96.78% character-level Macro-F1 on Task 1, 91.07% accuracy on Task 2, and 78.02% accuracy on Task 3. The results show that combining contextual Arabic representations with retrieval-based evidence is effective for fine-grained hallucination detection and correction in Arabic Islamic content.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.129/
%P 1050-1054
Markdown (Informal)
[Yaqeen at IslamicEval 2026: From Contextual Detection to Retrieval-Based Verification and Canonical Correction of LLM-Generated Arabic Islamic Citations](https://aclanthology.org/2026.arabicnlp-sharedtasks.129/) (Alsuhaibani & Alkaoud, ArabicNLP 2026)
ACL