Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs
This work addresses the problem of inadequate evaluation benchmarks for Korean LLMs, which is crucial for researchers and developers in Korean natural language processing, though it is incremental as it builds on an existing leaderboard.
The authors tackled the limitations of the Open Ko-LLM Leaderboard by proposing an improved version, Open Ko-LLM Leaderboard2, which replaces original benchmarks with tasks aligned with real-world capabilities and introduces four new native Korean benchmarks to better capture the language's intricacies, aiming to provide a more meaningful evaluation for Korean LLMs.
The Open Ko-LLM Leaderboard has been instrumental in benchmarking Korean Large Language Models (LLMs), yet it has certain limitations. Notably, the disconnect between quantitative improvements on the overly academic leaderboard benchmarks and the qualitative impact of the models should be addressed. Furthermore, the benchmark suite is largely composed of translated versions of their English counterparts, which may not fully capture the intricacies of the Korean language. To address these issues, we propose Open Ko-LLM Leaderboard2, an improved version of the earlier Open Ko-LLM Leaderboard. The original benchmarks are entirely replaced with new tasks that are more closely aligned with real-world capabilities. Additionally, four new native Korean benchmarks are introduced to better reflect the distinct characteristics of the Korean language. Through these refinements, Open Ko-LLM Leaderboard2 seeks to provide a more meaningful evaluation for advancing Korean LLMs.