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# MileBench
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We introduce MileBench, a pioneering benchmark designed to test the **M**ult**I**modal **L**ong-cont**E**xt capabilities of MLLMs.
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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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![images](./images/stat1.png)
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# MileBench
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## Introduction
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We introduce MileBench, a pioneering benchmark designed to test the **M**ult**I**modal **L**ong-cont**E**xt capabilities of MLLMs.
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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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<img src="./images/MileBench.png" width = "600" height = "500" alt="MileBench" align=center />
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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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<img src="./images/stat1.png" width = "300" height = "200" alt="stat1" align=center /><img src="./images/stat2.png" width = "300" height = "200" alt="stat1" align=center />
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## How to use?
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Please download MileBench.zip and refer to [Code for MileBench](https://github.com/MileBench/MileBench?tab=readme-ov-file#-dataset-preparation).
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## Citation
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If you find this project useful in your research, please consider cite:
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```BibTeX
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```
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