metadata
license: cc-by-2.0
dataset_info:
features:
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dtype: int32
- name: task_instruction
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- name: task_instance
struct:
- name: context
dtype: string
- name: images_path
sequence: string
- name: choice_list
sequence: string
- name: combined_1_images
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- name: response
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download_size: 12035444
dataset_size: 26288285
configs:
- config_name: default
data_files:
- split: ActionLocalization_test
path: preview/ActionLocalization_test-*
- split: ActionLocalization_adv
path: preview/ActionLocalization_adv-*
- split: ActionPrediction_test
path: preview/ActionPrediction_test-*
- split: ActionPrediction_adv
path: preview/ActionPrediction_adv-*
- split: ActionSequence_test
path: preview/ActionSequence_test-*
- split: ActionSequence_adv
path: preview/ActionSequence_adv-*
- split: ALFRED_test
path: preview/ALFRED_test-*
- split: ALFRED_adv
path: preview/ALFRED_adv-*
- split: CharacterOrder_test
path: preview/CharacterOrder_test-*
- split: CharacterOrder_adv
path: preview/CharacterOrder_adv-*
- split: CLEVR_Change_test
path: preview/CLEVR_Change_test-*
- split: CLEVR_Change_adv
path: preview/CLEVR_Change_adv-*
- split: CounterfactualInference_test
path: preview/CounterfactualInference_test-*
- split: CounterfactualInference_adv
path: preview/CounterfactualInference_adv-*
- split: DocVQA_test
path: preview/DocVQA_test-*
- split: DocVQA_adv
path: preview/DocVQA_adv-*
- split: EgocentricNavigation_test
path: preview/EgocentricNavigation_test-*
- split: EgocentricNavigation_adv
path: preview/EgocentricNavigation_adv-*
- split: GPR1200_test
path: preview/GPR1200_test-*
- split: IEdit_test
path: preview/IEdit_test-*
- split: IEdit_adv
path: preview/IEdit_adv-*
- split: ImageNeedleInAHaystack_test
path: preview/ImageNeedleInAHaystack_test-*
- split: MMCoQA_test
path: preview/MMCoQA_test-*
- split: MMCoQA_adv
path: preview/MMCoQA_adv-*
- split: MovingAttribute_test
path: preview/MovingAttribute_test-*
- split: MovingAttribute_adv
path: preview/MovingAttribute_adv-*
- split: MovingDirection_test
path: preview/MovingDirection_test-*
- split: MovingDirection_adv
path: preview/MovingDirection_adv-*
- split: MultiModalQA_test
path: preview/MultiModalQA_test-*
- split: MultiModalQA_adv
path: preview/MultiModalQA_adv-*
- split: nuscenes_test
path: preview/nuscenes_test-*
- split: nuscenes_adv
path: preview/nuscenes_adv-*
- split: ObjectExistence_test
path: preview/ObjectExistence_test-*
- split: ObjectExistence_adv
path: preview/ObjectExistence_adv-*
- split: ObjectInteraction_test
path: preview/ObjectInteraction_test-*
- split: ObjectInteraction_adv
path: preview/ObjectInteraction_adv-*
- split: ObjectShuffle_test
path: preview/ObjectShuffle_test-*
- split: ObjectShuffle_adv
path: preview/ObjectShuffle_adv-*
- split: OCR_VQA_test
path: preview/OCR_VQA_test-*
- split: OCR_VQA_adv
path: preview/OCR_VQA_adv-*
- split: SceneTransition_test
path: preview/SceneTransition_test-*
- split: SceneTransition_adv
path: preview/SceneTransition_adv-*
- split: SlideVQA_test
path: preview/SlideVQA_test-*
- split: SlideVQA_adv
path: preview/SlideVQA_adv-*
- split: Spot_the_Diff_test
path: preview/Spot_the_Diff_test-*
- split: Spot_the_Diff_adv
path: preview/Spot_the_Diff_adv-*
- split: StateChange_test
path: preview/StateChange_test-*
- split: StateChange_adv
path: preview/StateChange_adv-*
- split: TextNeedleInAHaystack_test
path: preview/TextNeedleInAHaystack_test-*
- split: TQA_test
path: preview/TQA_test-*
- split: TQA_adv
path: preview/TQA_adv-*
- split: WebQA_test
path: preview/WebQA_test-*
- split: WebQA_adv
path: preview/WebQA_adv-*
- split: WikiVQA_test
path: preview/WikiVQA_test-*
- split: WikiVQA_adv
path: preview/WikiVQA_adv-*
task_categories:
- visual-question-answering
- question-answering
- text-generation
- image-to-text
- video-classification
language:
- en
tags:
- Long-context
- MLLM
- VLM
- LLM
- Benchmark
pretty_name: MileBench
size_categories:
- 1K<n<10K
MileBench
Introduction
We introduce MileBench, a pioneering benchmark designed to test the MultImodal Long-contExt capabilities of MLLMs. This benchmark comprises not only multimodal long contexts, but also multiple tasks requiring both comprehension and generation. 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
To construct our evaluation sets, we gather 6,440 multimodal long-context samples from 21 pre-existing or self-constructed datasets, 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.
How to use?
Please download MileBench_part*.tar.gz and unzip them using the following command.
for file in MileBench_part*.tar.gz
do
tar -xzvf "$file"
done
Then please refer to Code for MileBench to evaluate.
Links
- Homepage: MileBench Homepage
- Repository: MileBench GitHub
- Paper: Arxiv
- Point of Contact: Dingjie Song
Citation
If you find this project useful in your research, please consider citing:
@article{song2024milebench,
title={MileBench: Benchmarking MLLMs in Long Context},
author={Song, Dingjie and Chen, Shunian and Chen, Guiming Hardy and Yu, Fei and Wan, Xiang and Wang, Benyou},
journal={arXiv preprint arXiv:2404.18532},
year={2024}
}