Summary of Understanding Llm Development Through Longitudinal Study: Insights From the Open Ko-llm Leaderboard, by Chanjun Park et al.
Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard
by Chanjun Park, Hyeonwoo Kim
First submitted to arxiv on: 5 Sep 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 The paper presents a longitudinal study that investigates the development of Korean large language models (LLMs) on the Open Ko-LLM Leaderboard over an eleven-month period. The researchers aim to provide a more comprehensive understanding of the progression in improving LLM performance across diverse tasks. The study is guided by three primary research questions, including what specific challenges exist in improving LLM performance, how model size impacts task performance correlations, and how patterns in leaderboard rankings have shifted over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Korean language models improve over time on a special leaderboard. By analyzing 1,769 models for eleven months, researchers want to understand the challenges they face, how bigger models do better, and if leaderboards change over time. This helps us learn more about these language models and how we can use them. |
