@inproceedings{yang-etal-2025-probench,
    title = "{P}ro{B}ench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks",
    author = "Yang, Yan  and
      Li, Dongxu  and
      Wu, Haoning  and
      Chen, Bei  and
      Liu, Liu  and
      Pan, Liyuan  and
      Li, Junnan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.568/",
    pages = "10883--10892",
    ISBN = "979-8-89176-256-5",
    abstract = "Solving expert-level multimodal tasks is a key milestone in general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to evolve, evaluation of frontier multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning. ProBench consists of 4,000 high-quality samples independently collected from professionals based on their productivity demands. It spans across 10 fields and 56 sub-fields, including science, arts, humanities, coding, mathematics, and creative writing. Experimentally, we evaluate and compare 24 latest models using MLLM-as-a-Judge. Our results reveal that although the best open-source models rival the proprietary ones, they all face significant challenges in visual perception, textual understanding, domain knowledge, and advanced reasoning. Our benchmark is publicly accessible at \url{TBC}."
}