Open Ko-llm Leaderboard: Evaluating Large Language Models In Korean With Ko-h5 Benchmark

Park Chanjun, Kim Hyeonwoo, Kim Dahyun, Cho Seonghwan, Kim Sanghoon, Lee Sukyung, Kim Yungi, Lee Hwalsuk. Arxiv 2024

[Paper]    
Reinforcement Learning Tools

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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