Won: Establishing Best Practices for Korean Financial NLP
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912289448263680 |
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| author | Son, Guijin Ko, Hyunwoo Jung, Haneral Hwang, Chami |
| author_facet | Son, Guijin Ko, Hyunwoo Jung, Haneral Hwang, Chami |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Won: Establishing Best Practices for Korean Financial NLP Son, Guijin Ko, Hyunwoo Jung, Haneral Hwang, Chami Computation and Language 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. |
| title | Won: Establishing Best Practices for Korean Financial NLP |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.17963 |