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@@ -148,6 +148,8 @@ We evaluated the models using 100 examples from the dev split.
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### Japanese MT Bench
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| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
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| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 4.93 | 1.50 | 4.70 | 7.80 | 1.55 | 2.60 | 7.80 | 6.10 | 7.40 |
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### Japanese MT Bench
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We evaluated the models using `gpt-4-0613`. Please see the [codes](https://github.com/llm-jp/llm-leaderboard/tree/main) for details.
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| Model name | average | coding | extraction | humanities | math | reasoning | roleplay | stem | writing |
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| :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| [llm-jp-3-1.8b-instruct](https://huggingface.co/llm-jp/llm-jp-3-1.8b-instruct) | 4.93 | 1.50 | 4.70 | 7.80 | 1.55 | 2.60 | 7.80 | 6.10 | 7.40 |
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