VISAI-AI/nitibench

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The declared licence (mit) is classified as permissive.

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@inproceedings{akarajaradwong-etal-2025-nitibench,
    title = "{N}iti{B}ench: Benchmarking {LLM} Frameworks on {T}hai Legal Question Answering Capabilities",
    author = "Akarajaradwong, Pawitsapak  and
      Pothavorn, Pirat  and
      Chaksangchaichot, Chompakorn  and
      Tasawong, Panuthep  and
      Nopparatbundit, Thitiwat  and
      Pratai, Keerakiat  and
      Nutanong, Sarana",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1739/",
    doi = "10.18653/v1/2025.emnlp-main.1739",
    pages = "34292--34315",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) show promise in legal question answering (QA), yet Thai legal QA systems face challenges due to limited data and complex legal structures. We introduce NitiBench, a novel benchmark featuring two datasets: (1) NitiBench-CCL, covering Thai financial laws, and (2) NitiBench-Tax, containing Thailand{'}s official tax rulings. Our benchmark also consists of specialized evaluation metrics suited for Thai legal QA. We evaluate retrieval-augmented generation (RAG) and long-context LLM (LCLM) approaches across three key dimensions: (1) the b

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dataset_licence({ "id": "VISAI-AI/nitibench" })
signal: "permissive"
stated_licence: "mit"
sources: 0