  In an era defined by the explosive growth of data and rapid technological
advancements, Multimodal Large Language Models (MLLMs) stand at the forefront
of artificial intelligence (AI) systems. Designed to seamlessly integrate
diverse data types-including text, images, videos, audio, and physiological
sequences-MLLMs address the complexities of real-world applications far beyond
the capabilities of single-modality systems. In this paper, we systematically
sort out the applications of MLLM in multimodal tasks such as natural language,
vision, and audio. We also provide a comparative analysis of the focus of
different MLLMs in the tasks, and provide insights into the shortcomings of
current MLLMs, and suggest potential directions for future research. Through
these discussions, this paper hopes to provide valuable insights for the
further development and application of MLLM.
