# Optimized Quran Passage Retrieval Using an Expanded QA Dataset and Fine-Tuned Language Models

arXiv: https://arxiv.org/abs/2412.11431 · HTML: https://mlsanity.com/paper/2412.11431/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [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)
- [Learning from models beyond fine-tuning](https://mlsanity.com/paper/2310.08184/) (2023)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [A Benchmark Dataset with Larger Context for Non-Factoid Question Answering over Islamic Text](https://mlsanity.com/paper/2409.09844/) (2024)
- [TCE at Qur'an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur'anic QA](https://mlsanity.com/paper/2401.13060/) (2024)

## Built on by (most cited first, 5 of 5)

- [Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs](https://mlsanity.com/paper/2508.06103/) (2025)
- [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction](https://mlsanity.com/paper/2508.06971/) (2025)
- [Research on Violent Text Detection System Based on BERT-fasttext Model](https://mlsanity.com/paper/2412.16455/) (2024)
- [Improving Answer Extraction in Context-based Question Answering Systems Using LLMs](https://mlsanity.com/paper/2606.06197/) (2026)
- [What Makes a Good Fiqh Retriever? Answer Retrieval for Arabic Islamic Jurisprudence](https://mlsanity.com/paper/2608.20246/) (2026)

## Abstract (arXiv)

Understanding the deep meanings of the Qur'an and bridging the language gap between modern standard Arabic and classical Arabic is essential to improve the question-and-answer system for the Holy Qur'an. The Qur'an QA 2023 shared task dataset had a limited number of questions with weak model retrieval. To address this challenge, this work updated the original dataset and improved the model accuracy. The original dataset, which contains 251 questions, was reviewed and expanded to 629 questions with question diversification and reformulation, leading to a comprehensive set of 1895 categorized into single-answer, multi-answer, and zero-answer types. Extensive experiments fine-tuned transformer models, including AraBERT, RoBERTa, CAMeLBERT, AraELECTRA, and BERT. The best model, AraBERT-base, achieved a MAP@10 of 0.36 and MRR of 0.59, representing improvements of 63% and 59%, respectively, compared to the baseline scores (MAP@10: 0.22, MRR: 0.37). Additionally, the dataset expansion led to improvements in handling "no answer" cases, with the proposed approach achieving a 75% success rate for such instances, compared to the baseline's 25%. These results demonstrate the effect of dataset improvement and model architecture optimization in increasing the performance of QA systems for the Holy Qur'an, with higher accuracy, recall, and precision.