Summary of Mm-vet V2: a Challenging Benchmark to Evaluate Large Multimodal Models For Integrated Capabilities, by Weihao Yu et al.
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
by Weihao Yu, Zhengyuan Yang, Lingfeng Ren, Linjie Li, Jianfeng Wang, Kevin Lin, Chung-Ching Lin, Zicheng Liu, Lijuan Wang, Xinchao Wang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The MM-Vet benchmark has become a popular tool for evaluating large multimodal models. It assesses six core vision-language capabilities, including recognition, knowledge, and language generation. However, the current format is limited to single image-text pairs, which doesn’t reflect real-world scenarios that often involve interleaved images and texts. To address this limitation, the authors introduce MM-Vet v2, which includes a new capability called “image-text sequence understanding” that evaluates models’ ability to process sequences. The evaluation set size is also expanded while maintaining high quality samples. Using MM-Vet v2, the best-performing model is Claude 3.5 Sonnet with a score of 71.8, followed by GPT-4o with a score of 71.0. Among open-weight models, InternVL2-Llama3-76B leads with a score of 68.4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MM-Vet is a tool that helps us understand how well computer models can understand and work with both images and text. It’s like having a big test to see how good these models are at recognizing things, understanding language, and more. The problem is that the current version of MM-Vet only looks at one image or text at a time, which isn’t how we use computers in real life. To fix this, the authors created a new version called MM-Vet v2 that can look at multiple images and texts together. This helps us see how well the models really work. |
Keywords
» Artificial intelligence » Claude » Gpt
