Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction

--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 T ask, 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. The Holy Qur'an, revealed over 1,400 years ago, remains the primary source of guidance for over 1.8 billion Muslims worldwide. Beyond its religious significance, the Qur'an represents a masterpiece of Classical Arabic literature, containing profound linguistic, historical, and ethical insights that continue to be studied by scholars across multiple disciplines [1].