Summary of A Survey on Benchmarks Of Multimodal Large Language Models, by Jian Li et al.
A Survey on Benchmarks of Multimodal Large Language Models
by Jian Li, Weiheng Lu, Hao Fei, Meng Luo, Ming Dai, Min Xia, Yizhang Jin, Zhenye Gan, Ding Qi, Chaoyou Fu, Ying Tai, Wankou Yang, Yabiao Wang, Chengjie Wang
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive review of 200 benchmarks and evaluations for Multimodal Large Language Models (MLLMs) in various applications, including visual question answering, visual perception, understanding, and reasoning. The review focuses on different aspects such as perception and understanding, cognition and reasoning, specific domains, key capabilities, and other modalities. The authors argue that evaluation is a crucial discipline to support the development of MLLMs better. They also discuss the limitations of current evaluation methods for MLLMs and explore promising future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) do in different tasks, like answering questions about pictures or understanding what we’re saying when we talk about visual things. The researchers reviewed 200 tests to see which ones were most helpful for improving these language models. They think that testing is really important for making sure these models get better over time. They also talked about some problems with the way we test them now and suggested new ideas for how to do it better. |
Keywords
» Artificial intelligence » Question answering
