Update README.md

README.md

@@ -75,6 +75,22 @@ By using our released weights, codes, and demos, you agree to and comply with th
 
 ## Evaluation
 
+
+### Multilingual World Knowledge
+
+
+We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi.
+
+| Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e
+|-----| -----  | --- |  -- | ----- | ---- | --- | --- | --- |
+| GPT-3.5         | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41
+| Vistral-7B-chat | Mono  | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27
+| Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25
+| SailorLM        | Multi | 52.72 | 59.76 | 67.74 | 50.14 | ---   | 39.53 | 37.73
+| SeaLLM-7B-v2    | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52
+| SeaLLM-7B-v2.5  | Multi | 64.05 | 76.87 | 62.54 | 63.11 | 53.30 | 48.64 | 46.86
+
+
 ### Zero-shot CoT Multilingual Math Reasoning
 
 <!--
@@ -83,21 +99,16 @@ By using our released weights, codes, and demos, you agree to and comply with th
 ![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png)
 -->
 
-
-<details>
-<summary>See details on English and translated GSM8K and MATH with zero-shot reasoning</summary>
-<br>
-
 | Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th
 | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1
-| Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6
+| Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6.0
 | Vistral-7b-chat | 48.2 | 12.5 |  |  | 48.7 | 3.1 |  |  |  | 
-| Qwen1.5-7B-chat | 56.8 | 15.3 | 40 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 |
+| Qwen1.5-7B-chat | 56.8 | 15.3 | 40.0 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 | 4.7
 | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4
 | SeaLLM-7B-v2.5 | 78.5 | 34.9 | 51.3 | 22.1 | 72.3 | 30.2 | 71.5 | 30.1 | 62.0 | 28.4
 
-</details>
+
 
 Baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Vistral](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)).
 
@@ -113,25 +124,10 @@ Baselines were evaluated using their respective chat-template and system prompts
 | SeaLLM-7B-v2.5 | 58.0 | **64.8**
 
 
-### Multilingual World Knowledge
-
-
-We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi.
-
-| Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e
-|-----| -----  | --- |  -- | ----- | ---- | --- | --- | --- |
-| GPT-3.5         | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41
-| Vistral-7B-chat | Mono  | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27
-| Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25
-| SailorLM        | Multi | 52.72 | 59.76 | 67.74 | 50.14 | ---   | 39.53 | 37.73
-| SeaLLM-7B-v2    | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52
-| SeaLLM-7B-v2.5  | Multi | 64.05 | 76.87 | 62.54 | 63.11 | 53.30 | 48.64 | 46.86
-
 
 ### Sea-Bench
 
-Not ready
-
+![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png)
 
 
 ### Usage