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Multimodal Robustness Benchmark

The MMR benchmark is designed to evaluate MLLMs' comprehension of visual content and robustness against misleading questions, ensuring models truly leverage multimodal inputs rather than relying solely on textual reasoning. The MMR-data is generated to enhance MLLMs' understanding capability and robustness by providing a training set with paired positive and negative visual question-answer samples.

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Cite this as

Yexin Liu, Zhengyang Liang, Yueze Wang, Muyang He, Jian Li, Bo Zhao (2024). Dataset: Multimodal Robustness Benchmark. https://doi.org/10.57702/hvw2a0mw

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2406.10638
Author	Yexin Liu
More Authors	Zhengyang Liang Yueze Wang Muyang He Jian Li Bo Zhao
Homepage	https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark