@inproceedings{eltanbouly-etal-2026-makes,
title = "What Makes a Good Fiqh Retriever? Answer Retrieval for {A}rabic Islamic Jurisprudence",
author = "Eltanbouly, Somaya and
Sbahi, Heba and
Rashwani, Samer and
Bouchekif, Abdessalam and
al-Khatib, Mutaz and
Gaben, Shahd and
Ghaly, Mohammed",
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",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-main.18/",
pages = "247--262",
ISBN = "979-8-89176-473-6",
abstract = "Retrieval-Augmented Generation is used for Islamic question answering, but most systems are evaluated end-to-end, making retrieval failures difficult to isolate from generation failures. We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question. We build a retrieval test collection for Arabic fiqh and use it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies. The best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553. Hybrid retrieval provides limited gains for strong models, whereas madhhab-aware filtering more than doubles MRR@5 on school-specific questions. We further present an error analysis showing that the main challenge is distinguishing answer-bearing passages from topically similar passages that do not contain the requested ruling. The best-performing model and fine-tuning dataset are publicly available at https://huggingface.co/collections/somayaeltanbouly/badr-arabic-fiqh-retrieval"
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<abstract>Retrieval-Augmented Generation is used for Islamic question answering, but most systems are evaluated end-to-end, making retrieval failures difficult to isolate from generation failures. We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question. We build a retrieval test collection for Arabic fiqh and use it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies. The best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553. Hybrid retrieval provides limited gains for strong models, whereas madhhab-aware filtering more than doubles MRR@5 on school-specific questions. We further present an error analysis showing that the main challenge is distinguishing answer-bearing passages from topically similar passages that do not contain the requested ruling. The best-performing model and fine-tuning dataset are publicly available at https://huggingface.co/collections/somayaeltanbouly/badr-arabic-fiqh-retrieval</abstract>
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%0 Conference Proceedings
%T What Makes a Good Fiqh Retriever? Answer Retrieval for Arabic Islamic Jurisprudence
%A Eltanbouly, Somaya
%A Sbahi, Heba
%A Rashwani, Samer
%A Bouchekif, Abdessalam
%A al-Khatib, Mutaz
%A Gaben, Shahd
%A Ghaly, Mohammed
%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
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-473-6
%F eltanbouly-etal-2026-makes
%X Retrieval-Augmented Generation is used for Islamic question answering, but most systems are evaluated end-to-end, making retrieval failures difficult to isolate from generation failures. We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question. We build a retrieval test collection for Arabic fiqh and use it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies. The best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553. Hybrid retrieval provides limited gains for strong models, whereas madhhab-aware filtering more than doubles MRR@5 on school-specific questions. We further present an error analysis showing that the main challenge is distinguishing answer-bearing passages from topically similar passages that do not contain the requested ruling. The best-performing model and fine-tuning dataset are publicly available at https://huggingface.co/collections/somayaeltanbouly/badr-arabic-fiqh-retrieval
%U https://aclanthology.org/2026.arabicnlp-main.18/
%P 247-262
Markdown (Informal)
[What Makes a Good Fiqh Retriever? Answer Retrieval for Arabic Islamic Jurisprudence](https://aclanthology.org/2026.arabicnlp-main.18/) (Eltanbouly et al., ArabicNLP 2026)
ACL