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  "access_rights": "",
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  "author": "Yexin Liu",
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  "author": "Yexin Liu",
            
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  "author_email": "",
            
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  "doi": "10.57702/hvw2a0mw",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Zhengyang Liang",
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      "extra_author": "Zhengyang Liang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Yueze Wang",
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      "extra_author": "Yueze Wang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Muyang He",
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      "extra_author": "Muyang He",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jian Li",
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      "extra_author": "Jian Li",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Bo Zhao",
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      "extra_author": "Bo Zhao",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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  ],
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      "description": "",
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      "description": "",
            
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      "title": "Multimodal Learning"
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      "title": "Multimodal Learning"
            
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      "title": "Question Answering"
            
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      "description": "",
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      "name": "robustness",
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      "name": "robustness",
            
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      "title": "Robustness"
            
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"https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark",
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"https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark",
            
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  "name": "multimodal-robustness-benchmark",
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  "name": "multimodal-robustness-benchmark",
            
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  "notes": "The MMR benchmark is designed to evaluate MLLMs' 
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  "notes": "The MMR benchmark is designed to evaluate MLLMs' 
            
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comprehension of visual content and robustness against misleading 
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comprehension of visual content and robustness against misleading 
            
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questions, ensuring models truly leverage multimodal inputs rather 
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questions, ensuring models truly leverage multimodal inputs rather 
            
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than relying solely on textual reasoning. The MMR-data is generated to 
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than relying solely on textual reasoning. The MMR-data is generated to 
            
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enhance MLLMs' understanding capability and robustness by providing a 
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enhance MLLMs' understanding capability and robustness by providing a 
            
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training set with paired positive and negative visual question-answer 
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training set with paired positive and negative visual question-answer 
            
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samples.",
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samples.",
            
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    {
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      "name": "Visual Question Answering",
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  ],
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  "title": "Multimodal Robustness Benchmark",
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  "title": "Multimodal Robustness Benchmark",
            
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}