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Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark (2405.20574v2)
Published 31 May 2024 in cs.CL and cs.AI
Abstract: This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating 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.
- Chanjun Park (49 papers)
- Hyeonwoo Kim (13 papers)
- Dahyun Kim (21 papers)
- Seonghwan Cho (2 papers)
- Sanghoon Kim (19 papers)
- Sukyung Lee (8 papers)
- Yungi Kim (13 papers)
- Hwalsuk Lee (10 papers)