Summary of Open Ko-llm Leaderboard: Evaluating Large Language Models in Korean with Ko-h5 Benchmark, by Chanjun Park et al.
Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark
by Chanjun Park, Hyeonwoo Kim, Dahyun Kim, Seonghwan Cho, Sanghoon Kim, Sukyung Lee, Yungi Kim, Hwalsuk Lee
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a crucial evaluation framework for Large Language Models (LLMs) in Korean, introducing the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark. By incorporating private test sets, mirroring the English Open LLM Leaderboard, and analyzing data leakage and temporal trends within the Ko-H5 benchmark, the authors demonstrate the benefits of a robust evaluation framework that has been well-received by the Korean LLM community. The study highlights the need to expand beyond set benchmarks, emphasizing the importance of linguistic diversity in LLM evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool for testing language models that are good at understanding Korean language. They make this tool similar to another one used for English language models and show how private test sets can be helpful. The authors also study how scores change over time and find that there’s a need to go beyond just using set benchmarks. This is important because it will help us have more diverse languages represented in these models. |
