{
 "id": "Donya/ISN",
 "owner": "Donya",
 "name": "ISN",
 "downloads": 327,
 "likes": 0,
 "last_modified": "2026-04-03T09:53:59.000Z",
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 "licence_name": null,
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 "tasks": [
  "text-classification"
 ],
 "citation": "@inproceedings{saffari-etal-2025-introduce,\n    title = \"Can {I} Introduce My Boyfriend to My Grandmother? Evaluating Large Language Models Capabilities on {I}ranian Social Norm Classification\",\n    author = \"Saffari, Hamidreza  and\n      Shafiei, Mohammadamin  and\n      Rooein, Donya  and\n      Pierri, Francesco  and\n      Nozza, Debora\",\n    editor = \"Chiruzzo, Luis  and\n      Ritter, Alan  and\n      Wang, Lu\",\n    booktitle = \"Findings of the Association for Computational Linguistics: NAACL 2025\",\n    month = apr,\n    year = \"2025\",\n    address = \"Albuquerque, New Mexico\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-naacl.337/\",\n    doi = \"10.18653/v1/2025.findings-naacl.337\",\n    pages = \"6075--6089\",\n    ISBN = \"979-8-89176-195-7\",\n    abstract = \"Creating globally inclusive AI systems demands datasets reflecting diverse social norms. Iran, with its unique cultural blend, offers an ideal case study, with Farsi adding linguistic complexity. In this work, we introduce the Iranian Social Norms (ISN) dataset, a novel collection of 1,699 Iranian social norms, including environments, demographic features, and scope annotation, alongside English translations. Our evaluation of 6 Large Language Models (LLMs) in classifying Iranian social norms, using a variety of prompts, uncovered critical insights into the impact of geographic and linguistic context. Results revealed a substantial performance gap in LLMs' compr",
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 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "994cca05e392ca7cfb6f12a4b3739e11d93d108c",
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