MULTI: Multimodal Understanding Leaderboard with Text and Images
<|reference_start|>MULTI: Multimodal Understanding Leaderboard with Text and Images: Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context. In this paper, we present MULTI as a cutting-edge benchmark for evaluating MLLMs on understanding complex tables and images, and reasoning with long context. MULTI provides multimodal inputs and requires responses that are either precise or open-ended, reflecting real-life examination styles. MULTI includes over 18,000 questions and challenges MLLMs with a variety of tasks, ranging from formula derivation to image detail analysis and cross-modality reasoning. We also introduce MULTI-Elite, a 500-question selected hard subset, and MULTI-Extend, with more than 4,500 external knowledge context pieces. Our evaluation indicates significant potential for MLLM advancement, with GPT-4V achieving a 63.7% accuracy rate on MULTI, in contrast to other MLLMs scoring between 28.5% and 55.3%. MULTI serves not only as a robust evaluation platform but also paves the way for the development of expert-level AI.<|reference_end|>
@article{zhu2024multi:,
    title={MULTI: Multimodal Understanding Leaderboard with Text and Images},
    author={Zichen Zhu, Yang Xu, Lu Chen, Jingkai Yang, Yichuan Ma, Yiming Sun,
  Hailin Wen, Jiaqi Liu, Jinyu Cai, Yingzi Ma, Situo Zhang, Zihan Zhao,
  Liangtai Sun, Kai Yu},
    journal={arXiv preprint arXiv:2402.03173},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03173},
    primaryClass={cs.CL cs.AI cs.CV}
}