A Survey on Benchmarks of Multimodal Large Language Models
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912017169776640 |
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| author | Li, Jian Lu, Weiheng Fei, Hao Luo, Meng Dai, Ming Xia, Min Jin, Yizhang Gan, Zhenye Qi, Ding Fu, Chaoyou Tai, Ying Yang, Wankou Wang, Yabiao Wang, Chengjie |
| author_facet | Li, Jian Lu, Weiheng Fei, Hao Luo, Meng Dai, Ming Xia, Min Jin, Yizhang Gan, Zhenye Qi, Ding Fu, Chaoyou Tai, Ying Yang, Wankou Wang, Yabiao Wang, Chengjie |
| contents | Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine MLLMs from multiple perspectives. This paper presents a comprehensive review of 200 benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, (3)specific domains, (4)key capabilities, and (5)other modalities. Finally, we discuss the limitations of the current evaluation methods for MLLMs and explore promising future directions. Our key argument is that evaluation should be regarded as a crucial discipline to support the development of MLLMs better. For more details, please visit our GitHub repository: https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08632 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Benchmarks of Multimodal Large Language Models Li, Jian Lu, Weiheng Fei, Hao Luo, Meng Dai, Ming Xia, Min Jin, Yizhang Gan, Zhenye Qi, Ding Fu, Chaoyou Tai, Ying Yang, Wankou Wang, Yabiao Wang, Chengjie Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine MLLMs from multiple perspectives. This paper presents a comprehensive review of 200 benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, (3)specific domains, (4)key capabilities, and (5)other modalities. Finally, we discuss the limitations of the current evaluation methods for MLLMs and explore promising future directions. Our key argument is that evaluation should be regarded as a crucial discipline to support the development of MLLMs better. For more details, please visit our GitHub repository: https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey. |
| title | A Survey on Benchmarks of Multimodal Large Language Models |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.08632 |