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README.md

@@ -37,7 +37,7 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 |      MME<sub>sum</sub>       |    1686.1    |    1508.0    |        2053.6        |    2064.1    |
 |         RealWorldQA          |     55.2     |     58.8     |         60.1         |     60.7     |
 |     AI2D<sub>test</sub>      |     68.3     |     76.7     |         76.9         |     78.9     |
-|      MMMU<sub>val</sub>      |     34.9     |     40.4     |         43.3         |     47.0     |
+|      MMMU<sub>val</sub>      |     34.9     | 40.4 / 46.1  |     43.3 / 45.1      | 47.0 / 48.3  |
 |  MMBench-EN<sub>test</sub>   |     71.0     |     73.6     |         76.2         |     78.6     |
 |  MMBench-CN<sub>test</sub>   |     63.6     |      -       |         70.3         |     73.9     |
 |    CCBench<sub>dev</sub>     |     29.6     |     24.1     |         58.8         |     66.5     |
@@ -47,7 +47,9 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 |   HallBench<sub>avg</sub>    |     32.2     |     39.0     |         42.8         |     41.9     |
 | MathVista<sub>testmini</sub> |     28.7     |     44.5     |         53.7         |     58.6     |
 
-- We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
+- We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
+
+- For MMMU, we report both the original scores (left side: evaluated using the InternVL codebase for InternVL series models, and sourced from technical reports or webpages for other models) and the VLMEvalKit scores (right side: collected from the OpenCompass leaderboard).
 
 - Please note that evaluating the same model using different testing toolkits like InternVL and VLMEvalKit can result in slight differences, which is normal. Updates to code versions and variations in environment and hardware can also cause minor discrepancies in results.
 
@@ -535,7 +537,7 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 |      MME<sub>sum</sub>       |    1686.1    |    1508.0    |        2053.6        |    2064.1    |
 |         RealWorldQA          |     55.2     |     58.8     |         60.1         |     60.7     |
 |     AI2D<sub>test</sub>      |     68.3     |     76.7     |         76.9         |     78.9     |
-|      MMMU<sub>val</sub>      |     34.9     |     40.4     |         43.3         |     47.0     |
+|      MMMU<sub>val</sub>      |     34.9     | 40.4 / 46.1  |     43.3 / 45.1      | 47.0 / 48.3  |
 |  MMBench-EN<sub>test</sub>   |     71.0     |     73.6     |         76.2         |     78.6     |
 |  MMBench-CN<sub>test</sub>   |     63.6     |      -       |         70.3         |     73.9     |
 |    CCBench<sub>dev</sub>     |     29.6     |     24.1     |         58.8         |     66.5     |
@@ -547,6 +549,8 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 
 - 我们同时使用 InternVL 和 VLMEvalKit 仓库进行模型评估。具体来说，DocVQA、ChartQA、InfoVQA、TextVQA、MME、AI2D、MMBench、CCBench、MMVet 和 SEED-Image 的结果是使用 InternVL 仓库测试的。MMMU、OCRBench、RealWorldQA、HallBench 和 MathVista 是使用 VLMEvalKit 进行评估的。
 
+- 对于MMMU，我们报告了原始分数（左侧：InternVL系列模型使用InternVL代码库评测，其他模型的分数来自其技术报告或网页）和VLMEvalKit分数（右侧：从OpenCompass排行榜收集）。
+
 - 请注意，使用不同的测试工具包（如 InternVL 和 VLMEvalKit）评估同一模型可能会导致细微差异，这是正常的。代码版本的更新、环境和硬件的变化也可能导致结果的微小差异。
 
 - 需要提到的是，我们报告的 MMVet 分数是使用 GPT-4-0613 作为评判模型评估的。不同版本的 GPT-4 会导致该数据集分数的显著变化。例如，使用 GPT-4-Turbo 会导致分数显著降低。