Open K o- LLM Leaderboard2: Bridging Foundational and Practical Evaluation for K orean LLM s (2025)

Abstract: 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.

## introduction the open ko-llm leaderboard was originally established as a critical evaluation platform to benchmark korean-specific large language models (llms) @xcite . its motivation stemmed from the growing need to adapt existing english-centric benchmarks to korean, thereby fostering the development of language models that can effectively handle the complexities of korean syntax and semantics. however, the leaderboard has faced significant limitations over time. for instance, as improvements in benchmark scores no longer translated to real-world advancements due to the overly academic nature of the benchmark suite, submission rates decreased as † corresponding author the leaderboard results were not as meaningful as before. the benchmark suite need tasks that correlate more with real-world performance. further, the leaderboard's tasks, primarily configured by translating english counterparts, do not sufficiently capture the nuances of the korean language. in fact, although the leaderboard was designed for korean llms, only one of the five benchmarks, ko-commongen v2, was specifically tailored for korean, highlighting a gap in its linguistic specificity. to address these challenges, we propose the open ko-llm leaderboard2. this nextgeneration framework replaces the previous benchmarks with a suite of tasks focusing on korean linguistic nuances and real-world applications. notably, the introduction of kornat benchmarks @xcite and practical, real-world evaluations like ko-ifeval @xcite and ko-gpqa @xcite ensures the leaderboard's continued relevance. furthermore, the shift toward fine-tuned models aligns with industry trends, enabling a more meaningful assessment of taskspecific performance in korean llms @xcite . ## open ko-llm leaderboard season 1 the open ko-llm leaderboard (season 1) @xcite was established to provide a comprehensive evaluation framework for korean-specific large language models (llms). its development was driven by two primary motivations: (i) ensuring alignment with the english open llm leaderboard to facilitate consistent and comparable evaluations across global and korean llms, and (ii) utilizing private test sets to prevent data contamination and ensure rigorous evaluation across a variety of models. the evaluation relied on the ko-h5 benchmark, which consisted of five tasks: ko-arc @xcite , ko-hellaswag @xcite , ko-mmlu @xcite , ko-truthfulqa @xcite , and ko-commongen v2 @xcite . while these tasks provided a foundational assessment of korean llms, four of the five benchmarks were direct translations from english datasets, limiting their linguistic specificity. only ko-commongen v2 was developed with a focus on korean, underscoring the need for more korean-centric benchmarks in future iterations. ## conclusion in this paper, we introduced open ko-llm leader-board2, addressing critical limitations from season 1 by incorporating nine benchmarks that better reflect the real-world capabilities of korean llms. our analysis of submission trends and performance correlations highlights the importance of aligning evaluations with real-world usage, especially through generation-based tasks. with these enhancements, open ko-llm leaderboard2 establishes a stronger framework for korean llm evaluation.