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.
@article{arxiv.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}
}
Comments
The training dataset is uploaded here: https://huggingface.co/datasets/KRX-Data/Won-Instruct. The model will be updated shortly