@inproceedings{mohamed-emad-elden-etal-2026-namaa-community,
title = "Namaa Community at {I}slamic{E}val 2026: Retrieval-Grounded Verification of Qur{'}anic and {H}adith Citations for Hallucination Identification",
author = "Mohamed Emad Elden, Fatimah and
Elhosiny, Israa and
Al Jallad, Khloud and
Nacar, Omer",
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.136/",
pages = "1098--1105",
ISBN = "979-8-89176-497-2",
abstract = "Large language models may generate inaccurate Islamic citations when answering religious questions. We present the Namaa Community system for Subtask{~}2 of IslamicEval{~}2026, which identifies hallucinations in Qur{'}anic verses (Ayah), hadith texts (matn), chains of narration (isnad), and claimed sources. We formulate the task as retrieval-grounded verification: citation spans are normalized and matched against the Qur{'}an and the six major hadith collections using character $n$-gram retrieval and edit-distance re-ranking. Type-specific verifiers assess Ayah and matn similarity, while claimed sources and isnads are checked against their parent citations. The deterministic pipeline requires no language model or GPU, and every decision is traceable to a canonical record. It achieves a macro accuracy of 0.846 on the development set, 17.9 percentage points above a per-type majority-class baseline, and 0.668 on the official blind test; we trace most of that shortfall to a submission-time configuration error and decompose the remainder by segment type. Our code and submissions are publicly available at \url{https://github.com/NAMAA-ORG/Namaa-Community-at-IslamicEval-2026}."
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<abstract>Large language models may generate inaccurate Islamic citations when answering religious questions. We present the Namaa Community system for Subtask 2 of IslamicEval 2026, which identifies hallucinations in Qur’anic verses (Ayah), hadith texts (matn), chains of narration (isnad), and claimed sources. We formulate the task as retrieval-grounded verification: citation spans are normalized and matched against the Qur’an and the six major hadith collections using character n-gram retrieval and edit-distance re-ranking. Type-specific verifiers assess Ayah and matn similarity, while claimed sources and isnads are checked against their parent citations. The deterministic pipeline requires no language model or GPU, and every decision is traceable to a canonical record. It achieves a macro accuracy of 0.846 on the development set, 17.9 percentage points above a per-type majority-class baseline, and 0.668 on the official blind test; we trace most of that shortfall to a submission-time configuration error and decompose the remainder by segment type. Our code and submissions are publicly available at https://github.com/NAMAA-ORG/Namaa-Community-at-IslamicEval-2026.</abstract>
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%0 Conference Proceedings
%T Namaa Community at IslamicEval 2026: Retrieval-Grounded Verification of Qur’anic and Hadith Citations for Hallucination Identification
%A Mohamed Emad Elden, Fatimah
%A Elhosiny, Israa
%A Al Jallad, Khloud
%A Nacar, Omer
%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 mohamed-emad-elden-etal-2026-namaa-community
%X Large language models may generate inaccurate Islamic citations when answering religious questions. We present the Namaa Community system for Subtask 2 of IslamicEval 2026, which identifies hallucinations in Qur’anic verses (Ayah), hadith texts (matn), chains of narration (isnad), and claimed sources. We formulate the task as retrieval-grounded verification: citation spans are normalized and matched against the Qur’an and the six major hadith collections using character n-gram retrieval and edit-distance re-ranking. Type-specific verifiers assess Ayah and matn similarity, while claimed sources and isnads are checked against their parent citations. The deterministic pipeline requires no language model or GPU, and every decision is traceable to a canonical record. It achieves a macro accuracy of 0.846 on the development set, 17.9 percentage points above a per-type majority-class baseline, and 0.668 on the official blind test; we trace most of that shortfall to a submission-time configuration error and decompose the remainder by segment type. Our code and submissions are publicly available at https://github.com/NAMAA-ORG/Namaa-Community-at-IslamicEval-2026.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.136/
%P 1098-1105
Markdown (Informal)
[Namaa Community at IslamicEval 2026: Retrieval-Grounded Verification of Qur’anic and Hadith Citations for Hallucination Identification](https://aclanthology.org/2026.arabicnlp-sharedtasks.136/) (Mohamed Emad Elden et al., ArabicNLP 2026)
ACL