Arxiv paper - MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

In this episode, we discuss MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks by Jiacheng Chen, Tianhao Liang, Sherman Siu, Zhengqing Wang, Kai Wang, Yubo Wang, Yuansheng Ni, Wang Zhu, Ziyan Jiang, Bohan Lyu, Dongfu Jiang, Xuan He, Yuan Liu, Hexiang Hu, Xiang Yue, Wenhu Chen. The paper introduces MEGA-BENCH, a comprehensive evaluation suite featuring over 500 real-world multimodal tasks to address diverse daily user needs. It includes more than 8,000 samples curated by 16 expert annotators, utilizing a variety of output formats such as numbers, phrases, and code instead of standard multiple-choice questions. MEGA-BENCH aims to provide high-quality, diverse data for cost-effective and accurate model evaluation across a wide range of multimodal tasks.