@inproceedings{son-etal-2025-finkrx,
    title = "{FINKRX}: Establishing Best Practices for {K}orean Financial {NLP}",
    author = "Son, Guijin  and
      Ko, Hyunwoo  and
      Jung, Hanearl  and
      Hwang, Chami",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.81/",
    pages = "1161--1174",
    ISBN = "979-8-89176-288-6",
    abstract = "In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for abouteight weeks, the leaderboard evaluated 1,119 submissions on a closed benchmark covering five MCQA categories: finance and accounting, stock price prediction, domestic company analysis, financial markets, and financial agent tasks and one open-ended qa task. Building on insights from these evaluations, we release an open instruction dataset of 80k instances and summarize widely used training strategies observed among top-performing models. Finally, we introduce FINKRX, a fully open and transparent LLM built using these best practices. We hope our contributions help advance the development of better and safer financial LLMs for Korean and other languages."
}