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  license: apache-2.0
 
 
 
 
 
 
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  license: apache-2.0
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+ task_categories:
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+ - visual-question-answering
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+ language:
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+ - en
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+ size_categories:
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+ - n<1K
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  ---
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+ ## Dataset Details
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+
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+ ### Dataset Description
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+
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+ MARVEL is a new comprehensive benchmark dataset that evaluates multi-modal large language models' abstract reasoning abilities in six patterns across five different task configurations, revealing significant performance gaps between humans and SoTA MLLMs.
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+
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+ ### Dataset Sources [optional]
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+
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+ - **Repository:** https://github.com/1171-jpg/MARVEL_AVR
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+ - **Paper [optional]:** https://arxiv.org/abs/2404.13591
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+ - **Demo [optional]:** https://marvel770.github.io/
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+
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+ ## Uses
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+
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+ Evaluations for multi-modal large language models' abstract reasoning abilities.
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+
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+ ## Dataset Structure
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+
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+ The directory **images** keeps all images, and the file **marvel_labels.jsonl** provides annotations and explanations for all questions.
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+
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+ ## Citation [optional]
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+
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+ **BibTeX:**
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+ ```
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+ @misc{jiang2024marvel,
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+ title={MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning},
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+ author={Yifan Jiang and Jiarui Zhang and Kexuan Sun and Zhivar Sourati and Kian Ahrabian and Kaixin Ma and Filip Ilievski and Jay Pujara},
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+ year={2024},
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+ eprint={2404.13591},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV}
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+ }
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+ ```