# Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs

arXiv: https://arxiv.org/abs/2508.06103 · HTML: https://mlsanity.com/paper/2508.06103/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [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)

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

- [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction](https://mlsanity.com/paper/2508.06971/) (2025)
- [Improving Answer Extraction in Context-based Question Answering Systems Using LLMs](https://mlsanity.com/paper/2606.06197/) (2026)

## Abstract (arXiv)

This paper presents two effective approaches for Extractive Question Answering (QA) on the Quran. It addresses challenges related to complex language, unique terminology, and deep meaning in the text. The second uses few-shot prompting with instruction-tuned large language models such as Gemini and DeepSeek. A specialized Arabic prompt framework is developed for span extraction. A strong post-processing system integrates subword alignment, overlap suppression, and semantic filtering. This improves precision and reduces hallucinations. Evaluations show that large language models with Arabic instructions outperform traditional fine-tuned models. The best configuration achieves a pAP10 score of 0.637. The results confirm that prompt-based instruction tuning is effective for low-resource, semantically rich QA tasks.