@inproceedings{hong-etal-2025-kmmlu,
    title = "From {KMMLU}-Redux to Pro: A Professional {K}orean Benchmark Suite for {LLM} Evaluation",
    author = "Hong, Seokhee  and
      Kim, Sunkyoung  and
      Son, Guijin  and
      Kim, Soyeon  and
      Hong, Yeonjung  and
      Lee, Jinsik",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1038/",
    doi = "10.18653/v1/2025.findings-emnlp.1038",
    pages = "19067--19096",
    ISBN = "979-8-89176-335-7",
    abstract = "The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applicability in real-world scenarios. In this paper, we introduce two Korean expert-level benchmarks. KMMLU-Redux, reconstructed from the existing KMMLU consists of questions from the Korean National Technical Qualification exams, with critical errors removed to enhance reliability. KMMLU-Pro is based on Korean National Professional Licensure exams to reflect professional knowledge in Korea. Our experiments demonstrate that these benchmarks comprehensively represent industrial knowledge in Korea."
}