In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for about eight 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 Won, 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.
View on arXiv@article{son2025_2503.17963,
title={ Won: Establishing Best Practices for Korean Financial NLP },
author={ Guijin Son and Hyunwoo Ko and Haneral Jung and Chami Hwang },
journal={arXiv preprint arXiv:2503.17963},
year={ 2025 }
}