# Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction Source: [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:33:44.062Z Authors: Mohamed Basem, Islam Oshallah, Ali Hamdi, Khaled Shaban, Hozaifa Kassab Published: 2025-08-09T12:37:19.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()