This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark, designed to evaluate Large Language Models (LLMs) in Korean. The authors address the lack of robust evaluation frameworks for Korean LLMs by establishing a robust framework that mirrors the English Open LLM Leaderboard. They incorporate private test sets to prevent data contamination and perform extensive analyses to highlight the benefits of this approach. Key findings include: 1. **Private Test Sets**: The private test sets used in the Ko-H5 benchmark have minimal overlap with popular training datasets, ensuring fair and robust evaluation. 2. **Correlation Studies**: Analysis of the Ko-H5 benchmark shows that the newly added Ko-CommonGen v2 dataset brings more diversity to the evaluation suite. 3. **Temporal Analysis**: Temporal analyses reveal insights into critical model sizes for rapid performance improvement and the saturation of certain task scores. 4. **Community Effort**: The paper calls for community efforts to improve the leaderboard, addressing issues such as model card documentation and model deletion. The authors conclude that the Open Ko-LLM Leaderboard and Ko-H5 Benchmark are essential tools for expanding the evaluation of LLMs in Korean, fostering linguistic diversity and advancing AI research. They also discuss limitations, such as the static nature of the benchmark and the need for more extensive temporal analyses, and emphasize the importance of ethical considerations in their research and evaluation processes.This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark, designed to evaluate Large Language Models (LLMs) in Korean. The authors address the lack of robust evaluation frameworks for Korean LLMs by establishing a robust framework that mirrors the English Open LLM Leaderboard. They incorporate private test sets to prevent data contamination and perform extensive analyses to highlight the benefits of this approach. Key findings include: 1. **Private Test Sets**: The private test sets used in the Ko-H5 benchmark have minimal overlap with popular training datasets, ensuring fair and robust evaluation. 2. **Correlation Studies**: Analysis of the Ko-H5 benchmark shows that the newly added Ko-CommonGen v2 dataset brings more diversity to the evaluation suite. 3. **Temporal Analysis**: Temporal analyses reveal insights into critical model sizes for rapid performance improvement and the saturation of certain task scores. 4. **Community Effort**: The paper calls for community efforts to improve the leaderboard, addressing issues such as model card documentation and model deletion. The authors conclude that the Open Ko-LLM Leaderboard and Ko-H5 Benchmark are essential tools for expanding the evaluation of LLMs in Korean, fostering linguistic diversity and advancing AI research. They also discuss limitations, such as the static nature of the benchmark and the need for more extensive temporal analyses, and emphasize the importance of ethical considerations in their research and evaluation processes.