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@@ -324,5 +324,12 @@ We introduce MileBench, a pioneering benchmark designed to test the **M**ult**I*
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  This benchmark comprises not only multimodal long contexts, but also multiple tasks requiring both comprehension and generation.
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  We establish two distinct evaluation sets, diagnostic and realistic, to systematically assess MLLMs’ long-context adaptation capacity and their ability to completetasks in long-context scenarios
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  To construct our evaluation sets, we gather 6,440 multimodal long-context samples from 21 pre-existing or self-constructed datasets,
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- with an average of 15.2 images and 422.3 words each, as depicted in the figure, and we categorize them into their respective subsets.
 
 
 
 
 
 
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  This benchmark comprises not only multimodal long contexts, but also multiple tasks requiring both comprehension and generation.
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  We establish two distinct evaluation sets, diagnostic and realistic, to systematically assess MLLMs’ long-context adaptation capacity and their ability to completetasks in long-context scenarios
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+ ![images](./images/MileBench.png)
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+
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  To construct our evaluation sets, we gather 6,440 multimodal long-context samples from 21 pre-existing or self-constructed datasets,
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+ with an average of 15.2 images and 422.3 words each, as depicted in the figure, and we categorize them into their respective subsets.
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+
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+ ![images](./images/stat1.png)
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+ ![images](./images/stat2.png)