Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916360287682560 |
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| author | Park, Chanjun Kim, Hyeonwoo Kim, Dahyun Cho, Seonghwan Kim, Sanghoon Lee, Sukyung Kim, Yungi Lee, Hwalsuk |
| author_facet | Park, Chanjun Kim, Hyeonwoo Kim, Dahyun Cho, Seonghwan Kim, Sanghoon Lee, Sukyung Kim, Yungi Lee, Hwalsuk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Computation and Language Artificial Intelligence 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. |
| title | Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.20574 |