MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
<|reference_start|>MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities: MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluation. MM-Vet assesses six core vision-language (VL) capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math. However, its question format is restricted to single image-text pairs, lacking the interleaved image and text sequences prevalent in real-world scenarios. To address this limitation, we introduce MM-Vet v2, which includes a new VL capability called "image-text sequence understanding", evaluating models' ability to process VL sequences. Furthermore, we maintain the high quality of evaluation samples while further expanding the evaluation set size. Using MM-Vet v2 to benchmark large multimodal models, we found that Claude 3.5 Sonnet is the best model with a score of 71.8, slightly outperforming GPT-4o which scored 71.0. Among open-weight models, InternVL2-Llama3-76B leads with a score of 68.4.<|reference_end|>
@article{yu2024mm-vet,
    title={MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models
  for Integrated Capabilities},
    author={Weihao Yu, Zhengyuan Yang, Linfeng Ren, Linjie Li, Jianfeng Wang,
  Kevin Lin, Chung-Ching Lin, Zicheng Liu, Lijuan Wang, Xinchao Wang},
    journal={arXiv preprint arXiv:2408.00765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00765},
    primaryClass={cs.CV cs.AI cs.CL}
}