{
 "id": "ai4bharat/MILU",
 "owner": "ai4bharat",
 "name": "MILU",
 "downloads": 1639,
 "likes": 27,
 "last_modified": "2026-09-08T17:22:17.000Z",
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 "tasks": [
  "multiple-choice",
  "question-answering"
 ],
 "citation": "@inproceedings{verma-etal-2025-milu,\n    title = \"{MILU}: A Multi-task {I}ndic Language Understanding Benchmark\",\n    author = \"Verma, Sshubam  and\n      Khan, Mohammed Safi Ur Rahman  and\n      Kumar, Vishwajeet  and\n      Murthy, Rudra  and\n      Sen, Jaydeep\",\n    editor = \"Chiruzzo, Luis  and\n      Ritter, Alan  and\n      Wang, Lu\",\n    booktitle = \"Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)\",\n    month = apr,\n    year = \"2025\",\n    address = \"Albuquerque, New Mexico\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.naacl-long.507/\",\n    doi = \"10.18653/v1/2025.naacl-long.507\",\n    pages = \"10076--10132\",\n    ISBN = \"979-8-89176-189-6\",\n    abstract = \"Evaluating Large Language Models (LLMs) in low-resource and linguistically diverse languages remains a significant challenge in NLP, particularly for languages using non-Latin scripts like those spoken in India. Existing benchmarks predominantly focus on English, leaving substantial gaps in assessing LLM capabilities in these languages. We introduce MILU, a Multi-task Indic Language Understanding Benchmark, a comprehensive evaluation benchmark designed to address this gap. MILU spans 8 domains and 41 subjects across 11 Indic languages, reflecting general and culturally specific knowledge. With an India-centric design, incorporates material ",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "846d7016558b2004386f30e59a2158731262492f",
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}