SeongHwan Cho
2024
Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark
Chanjun Park
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Hyeonwoo Kim
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Dahyun Kim
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SeongHwan Cho
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Sanghoon Kim
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Sukyung Lee
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Yungi Kim
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Hwalsuk Lee
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating Large Language Models (LLMs) in Korean. Incorporating private test sets while mirroring the English Open LLM Leaderboard, we establish a robust evaluation framework that has been well integrated in the Korean LLM community. We perform data leakage analysis that shows the benefit of private test sets along with a correlation study within the Ko-H5 benchmark and temporal analyses of the Ko-H5 score. Moreover, we present empirical support for the need to expand beyond set benchmarks. We hope the Open Ko-LLM Leaderboard sets precedent for expanding LLM evaluation to foster more linguistic diversity.
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Co-authors
- Chanjun Park 1
- Hyeonwoo Kim 1
- Dahyun Kim 1
- Sanghoon Kim 1
- Sukyung Lee 1
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