ai4bharat/MILU

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@inproceedings{verma-etal-2025-milu,
    title = "{MILU}: A Multi-task {I}ndic Language Understanding Benchmark",
    author = "Verma, Sshubam  and
      Khan, Mohammed Safi Ur Rahman  and
      Kumar, Vishwajeet  and
      Murthy, Rudra  and
      Sen, Jaydeep",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    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)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.naacl-long.507/",
    doi = "10.18653/v1/2025.naacl-long.507",
    pages = "10076--10132",
    ISBN = "979-8-89176-189-6",
    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

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dataset_licence({ "id": "ai4bharat/MILU" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0