Datasets:

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tmmluplus

@@ -2252,46 +2252,47 @@ We present TMMLU+, a traditional Chinese massive multitask language understandin
 The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models.
-## Leaderboard ( Last Updated : 2025/04/05 )
-                   Model                    | humanities | social sciences |   STEM   |  Others  | Average
------------------------------------------------------------------------------------------------------------
-openrouter/quasar-alpha                     |  70.38   |  83.07   |  87.89   |  76.42   |  79.44
-deepseek-v3                               |  73.19   |  81.93   |  82.93   |  74.41   |  78.11
-Qwen/Qwen2.5-72B-Instruct-Turbo             |  67.59   |  79.36   |  82.57   |  72.65   |  75.54
-gpt-4o-2024-08-06                           |  65.48   |  78.23   |  81.39   |  71.24   |  74.08
-claude-3-5-sonnet-20240620                  |  73.23   |  78.27   |  68.50   |  69.35   |  72.34
-gemini-2.0-flash-001                        |  67.81   |  75.24   |  74.79   |  65.92   |  70.94
-gemini-2.0-flash-lite-001                   |  65.26   |  75.12   |  72.73   |  65.50   |  69.65
-gemini-2.0-flash-lite-preview-02-05         |  64.66   |  73.48   |  70.00   |  63.90   |  68.01
-meta-llama/llama-3.2-90b-vision-instruct    |  68.93   |  75.77   |  67.19   |  59.79   |  67.92
-qwen/qwen2.5-vl-32b-instruct                |  62.93   |  79.24   |  63.61   |  65.16   |  67.73
-Qwen/QwQ-32B-Preview                        |  57.98   |  70.94   |  72.87   |  63.59   |  66.35
-claude-3-opus-20240229                      |  60.34   |  70.12   |  67.43   |  62.32   |  65.05
-gemini-1.5-pro                              |  61.84   |  70.29   |  66.18   |  60.30   |  64.65
-gpt-4o-mini-2024-07-18                      |  55.01   |  67.09   |  73.16   |  61.36   |  64.15
-mistralai/Mistral-Small-24B-Instruct-2501   |  54.56   |  68.32   |  73.25   |  59.25   |  63.85
-llama-3.1-70b-versatile                     |  64.94   |  70.14   |  58.63   |  61.33   |  63.76
-Qwen/Qwen2.5-7B-Instruct-Turbo              |  54.42   |  64.51   |  68.01   |  58.83   |  61.44
-yentinglin/Llama-3-Taiwan-8B-Instruct       |  61.51   |  67.61   |  52.05   |  58.60   |  59.94
-meta-llama/llama-4-scout                    |  53.86   |  61.83   |  60.76   |  57.78   |  58.56
-google/gemma-3-27b-it                       |  59.75   |  58.32   |  55.65   |  49.21   |  55.73
-claude-3-sonnet-20240229                    |  52.06   |  59.38   |  49.87   |  51.64   |  53.24
-meta-llama/llama-4-maverick                 |  52.53   |  49.88   |  54.82   |  51.41   |  52.16
-Qwen2-7B-Instruct                           |  55.66   |  66.40   |  27.18   |  55.32   |  51.14
-gemma2-9b-it                                |  45.38   |  55.76   |  49.89   |  48.92   |  49.99
-claude-3-haiku-20240307                     |  47.48   |  54.48   |  48.47   |  48.77   |  49.80
-gemini-1.5-flash                            |  42.99   |  53.42   |  53.47   |  46.56   |  49.11
-reka-flash                                  |  44.07   |  52.68   |  46.04   |  43.43   |  46.56
-meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo |  44.03   |  50.95   |  42.75   |  45.19   |  45.73
-mixtral-8x7b-32768                          |  44.75   |  50.34   |  32.60   |  43.76   |  42.86
-meta-llama/Llama-3-70b-chat-hf              |  37.50   |  47.02   |  34.44   |  39.51   |  39.62
-RekaAI/reka-flash-3                         |  34.76   |  40.70   |  42.98   |  35.37   |  38.45
-google/gemma-7b-it                          |  34.00   |  35.70   |  31.89   |  33.79   |  33.84
-reka-edge                                   |  31.84   |  39.40   |  30.02   |  32.36   |  33.41
-meta-llama/Llama-3-8b-chat-hf               |  28.91   |  34.19   |  31.52   |  31.79   |  31.60
-taide/Llama3-TAIDE-LX-8B-Chat-Alpha1        |  27.02   |  36.64   |  25.33   |  27.96   |  29.24
 ## How to use

 The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models.
+## Leaderboard ( Last Updated : 2025/04/06 )
+| Model | humanities | social sciences | STEM | Others | Average |
+| ----- | ---: | ---: | ---: | ---: | ---: |
+| openrouter/quasar-alpha | 70.38 | 83.07 | 87.89 | 76.42 | 79.44 |
+| deepseek-v3 | 73.19 | 81.93 | 82.93 | 74.41 | 78.11 |
+| Qwen/Qwen2.5-72B-Instruct-Turbo | 67.59 | 79.36 | 82.57 | 72.65 | 75.54 |
+| gpt-4o-2024-08-06 | 65.48 | 78.23 | 81.39 | 71.24 | 74.08 |
+| claude-3-5-sonnet-20240620 | 73.23 | 78.27 | 68.50 | 69.35 | 72.34 |
+| gemini-2.0-flash-001 | 67.81 | 75.24 | 74.79 | 65.92 | 70.94 |
+| gemini-2.0-flash-lite-001 | 65.26 | 75.12 | 72.73 | 65.50 | 69.65 |
+| gemini-2.0-flash-lite-preview-02-05 | 64.66 | 73.48 | 70.00 | 63.90 | 68.01 |
+| meta-llama/llama-3.2-90b-vision-instruct | 68.93 | 75.77 | 67.19 | 59.79 | 67.92 |
+| qwen/qwen2.5-vl-32b-instruct | 62.93 | 79.24 | 63.61 | 65.16 | 67.73 |
+| Qwen/QwQ-32B-Preview | 57.98 | 70.94 | 72.87 | 63.59 | 66.35 |
+| claude-3-opus-20240229 | 60.34 | 70.12 | 67.43 | 62.32 | 65.05 |
+| gemini-1.5-pro | 61.84 | 70.29 | 66.18 | 60.30 | 64.65 |
+| gpt-4o-mini-2024-07-18 | 55.01 | 67.09 | 73.16 | 61.36 | 64.15 |
+| mistralai/Mistral-Small-24B-Instruct-2501 | 54.56 | 68.32 | 73.25 | 59.25 | 63.85 |
+| llama-3.1-70b-versatile | 64.94 | 70.14 | 58.63 | 61.33 | 63.76 |
+| Qwen/Qwen2.5-7B-Instruct-Turbo | 54.42 | 64.51 | 68.01 | 58.83 | 61.44 |
+| yentinglin/Llama-3-Taiwan-8B-Instruct | 61.51 | 67.61 | 52.05 | 58.60 | 59.94 |
+| meta-llama/llama-4-scout | 53.86 | 61.83 | 60.76 | 57.78 | 58.56 |
+| google/gemma-3-27b-it | 59.75 | 58.32 | 55.65 | 49.21 | 55.73 |
+| claude-3-sonnet-20240229 | 52.06 | 59.38 | 49.87 | 51.64 | 53.24 |
+| meta-llama/llama-4-maverick | 52.53 | 49.88 | 54.82 | 51.41 | 52.16 |
+| Qwen2-7B-Instruct | 55.66 | 66.40 | 27.18 | 55.32 | 51.14 |
+| gemma2-9b-it | 45.38 | 55.76 | 49.89 | 48.92 | 49.99 |
+| claude-3-haiku-20240307 | 47.48 | 54.48 | 48.47 | 48.77 | 49.80 |
+| gemini-1.5-flash | 42.99 | 53.42 | 53.47 | 46.56 | 49.11 |
+| reka-flash | 44.07 | 52.68 | 46.04 | 43.43 | 46.56 |
+| meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo | 44.03 | 50.95 | 42.75 | 45.19 | 45.73 |
+| deepseek-reasoner | 0.00 | 0.00 | 89.94 | 84.71 | 43.66 |
+| mixtral-8x7b-32768 | 44.75 | 50.34 | 32.60 | 43.76 | 42.86 |
+| meta-llama/Llama-3-70b-chat-hf | 37.50 | 47.02 | 34.44 | 39.51 | 39.62 |
+| gemini-2.0-pro-exp-02-05 | 0.00 | 0.00 | 78.25 | 77.20 | 38.86 |
+| RekaAI/reka-flash-3 | 34.76 | 40.70 | 42.98 | 35.37 | 38.45 |
+| google/gemma-7b-it | 34.00 | 35.70 | 31.89 | 33.79 | 33.84 |
+| reka-edge | 31.84 | 39.40 | 30.02 | 32.36 | 33.41 |
+| meta-llama/Llama-3-8b-chat-hf | 28.91 | 34.19 | 31.52 | 31.79 | 31.60 |
+| taide/Llama3-TAIDE-LX-8B-Chat-Alpha1 | 27.02 | 36.64 | 25.33 | 27.96 | 29.24 |
 ## How to use