{
 "id": "QCRI/MultiNativQA",
 "owner": "QCRI",
 "name": "MultiNativQA",
 "downloads": 256,
 "likes": 3,
 "last_modified": "2026-03-31T17:41:51.000Z",
 "stated_licence": "cc-by-nc-sa-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "question-answering"
 ],
 "citation": "@inproceedings{hasan-etal-2025-nativqa,\n    title = \"{N}ativ{QA}: Multilingual Culturally-Aligned Natural Query for {LLM}s\",\n    author = \"Hasan, Md. Arid  and\n      Hasanain, Maram  and\n      Ahmad, Fatema  and\n      Laskar, Sahinur Rahman  and\n      Upadhyay, Sunaya  and\n      Sukhadia, Vrunda N  and\n      Kutlu, Mucahid  and\n      Chowdhury, Shammur Absar  and\n      Alam, Firoj\",\n    editor = \"Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher\",\n    booktitle = \"Findings of the Association for Computational Linguistics: ACL 2025\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-acl.770/\",\n    doi = \"10.18653/v1/2025.findings-acl.770\",\n    pages = \"14886--14909\",\n    ISBN = \"979-8-89176-256-5\",\n    abstract = \"Natural Question Answering (QA) datasets play a crucial role in evaluating the capabilities of large language models (LLMs), ensuring their effectiveness in real-world applications. Despite the numerous QA datasets that have been developed and some work done in parallel, there is a notable lack of a framework and large-scale region-specific datasets queried by native users in their own languages. This gap hinders effective benchmarking and the development of fine-tuned models for regional and cultural specificities. In this study, we propose a scalable, language-independent framework,",
 "signal": "restrictive",
 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
 "revision": "018bd34d17d234b9ba1a134a6a4b437b4767c176",
 "stated_evidence": "https://huggingface.co/datasets/QCRI/MultiNativQA/blob/018bd34d17d234b9ba1a134a6a4b437b4767c176/README.md",
 "file_evidence": "https://huggingface.co/datasets/QCRI/MultiNativQA/tree/018bd34d17d234b9ba1a134a6a4b437b4767c176",
 "card_nc": false
}