Year: 2025
Type: conference-paper
Abstract: 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 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.1
Authors: Guijin Son, Hyunwoo Ko, Hanearl Jung, Chami Hwang
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412886895