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Benchmarks of MLLMs: Survey

A Survey on Benchmarks of Multimodal Large Language Models [arXiv]

Jian Li¹, Weiheng Lu²*

¹Tencent, ²PKU

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📌 What is This Survey About?

Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine MLLMs from multiple perspectives. This paper presents a comprehensive review of 180 benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, (3)specific domains, (4)key capabilities, and (5)other modalities. Finally, we discuss the limitations of the current evaluation methods for MLLMs and explore promising future directions. Our key argument is that evaluation should be regarded as a crucial discipline to better support the development of MLLMs.

Summary of 180 MLLM Benchmarks

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