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2 claims from 1 paper

1. Computer Science › Multimodal Machine Learning Applications

   MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

   Xiang, Ni, Zheng et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2024

   The paper introduces MMMU, a college-level multimodal benchmark, and reports that 28 open-source models, GPT-4V and Gemini reach limited accuracy, which the authors read as room for improvement.

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   1. UncheckedThe MMMU benchmark contains 11.5K questions from college exams, quizzes and textbooks, spread across six broad academic disciplines.“MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and text-books, covering six core disciplines: Art & Design, Busi-ness, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.”
   2. UncheckedOn the MMMU benchmark, GPT-4V scored 56% and Gemini Ultra 59%, which the authors say leaves significant room for improvement.“Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement.”