---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
language:
- en
tags:
- Quran
- Tafseer
- Ahadith
- Question/Answering
- Larger_context
size_categories:
- 1K<n<10K
---
# 📚 LCQA-Islamic: A Benchmark Dataset with Larger Context for Non-Factoid QA over Islamic Texts

## Dataset Summary
This dataset provides a benchmark for **non-factoid question answering** over Islamic texts with an emphasis on **larger context retrieval**.  
It includes expert-curated QA pairs where the answers require reasoning across multi-sentence or paragraph-level contexts from authentic Islamic sources such as the Quran, Hadith, and Tafseer.

Designed to support **long-contextual Answer generation** models and **multi-passage reasoning** tasks.

## Supported Tasks and Leaderboards
- 📝 **Long-Form Question Answering**  
- 📚 **Multi-Passage Retrieval**  
- 🤖 Suitable for evaluation of **RAG systems** and **long-context LLMs**

## Languages
- 🌐 **English** 

## Dataset Structure
Each sample includes:
- `question` (string): The user query
- `context` (string): Retrieved passages from Islamic texts
- `answer` (string): Ground-truth answer curated by experts

## License
This dataset is licensed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license.  
- 🔒 Non-commercial use only  
- ✔️ Attribution required  
- 🔄 ShareAlike required  

Full license text: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)

## Citation
If you use this dataset in your research, please cite the following paper:

```bibtex
@article{qamar2024benchmark,
  title={A Benchmark Dataset with Larger Context for Non-Factoid Question Answering over Islamic Text},
  author={Qamar, Faiza and Latif, Seemab and Latif, Rabia},
  journal={arXiv preprint arXiv:2409.09844},
  year={2024}
}