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

arXiv: https://arxiv.org/abs/2508.06971 · HTML: https://mlsanity.com/paper/2508.06971/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [Neural Arabic Question Answering](https://mlsanity.com/paper/1906.05394/) (2019)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [ArabicaQA: A Comprehensive Dataset for Arabic Question Answering](https://mlsanity.com/paper/2403.17848/) (2024)
- [A Survey on Legal Question Answering Systems](https://mlsanity.com/paper/2110.07333/) (2021)
- [Optimized Quran Passage Retrieval Using an Expanded QA Dataset and Fine-Tuned Language Models](https://mlsanity.com/paper/2412.11431/) (2024)
- [Cross-Language Approach for Quranic QA](https://mlsanity.com/paper/2501.17449/) (2025)
- [A Multi-Layered Large Language Model Framework for Disease Prediction](https://mlsanity.com/paper/2502.00063/) (2025)
- [TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models](https://mlsanity.com/paper/2206.01550/) (2022)
- [Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs](https://mlsanity.com/paper/2508.06103/) (2025)

## Built on by (most cited first, 2 of 2)

- [Improving Answer Extraction in Context-based Question Answering Systems Using LLMs](https://mlsanity.com/paper/2606.06197/) (2026)
- [A Self Consistency Based Reranking for Narrative Question Answering](https://mlsanity.com/paper/2606.15741/) (2026)

## Abstract (arXiv)

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.