README.md

metadata

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

Citation

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

@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}
}