Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction
Abstract
Quranic Question Answering presents unique challenges due to the linguistic complexity of Classical Arabic and the semantic richness of religious texts. In this paper, we propose a novel two-stage framework that addresses both passage retrieval and answer extraction. For passage retrieval, we ensemble fine-tuned Arabic language models to achieve superior ranking performance. For answer extraction, we employ instruction-tuned large language models with few-shot prompting to overcome the limitations of fine-tuning on small datasets. Our approach achieves state-of-the-art results on the Quran QA 2023 Shared Task, with a MAP@10 of 0.3128 and MRR@10 of 0.5763 for retrieval, and a pAP@10 of 0.669 for extraction, substantially outperforming previous methods. These results demonstrate that combining model ensembling and instruction-tuned language models effectively addresses the challenges of low-resource question answering in specialized domains.
Cite
@article{arxiv.2508.06971,
title = {Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction},
author = {Mohamed Basem and Islam Oshallah and Ali Hamdi and Khaled Shaban and Hozaifa Kassab},
journal= {arXiv preprint arXiv:2508.06971},
year = {2025}
}
Comments
8 pages , 4 figures , Accepted in Aiccsa 2025 , https://conferences.sigappfr.org/aiccsa2025/