A Survey on Benchmarks of Multimodal Large Language Models
It provides a comprehensive survey for researchers and practitioners in AI, but it is incremental as it summarizes existing benchmarks without introducing new methods.
This paper reviews 200 benchmarks and evaluations for Multimodal Large Language Models (MLLMs), highlighting their performance in applications like visual question answering and reasoning, and argues that evaluation is crucial for supporting MLLM development.
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 200 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 support the development of MLLMs better. For more details, please visit our GitHub repository: https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey.