Multimodal Large Language Model (MLLM) recently has been a new rising
research hotspot, which uses powerful Large Language Models (LLMs) as a brain
to perform multimodal tasks. The surprising emergent capabilities of MLLM, such
as writing stories based on images and OCR-free math reasoning, are rare in
traditional methods, suggesting a potential path to artificial general
intelligence. In this paper, we aim to trace and summarize the recent progress
of MLLM. First of all, we present the formulation of MLLM and delineate its
related concepts. Then, we discuss the key techniques and applications,
including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning
(M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning
(LAVR). Finally, we discuss existing challenges and point out promising
research directions. In light of the fact that the era of MLLM has only just
begun, we will keep updating this survey and hope it can inspire more research.
An associated GitHub link collecting the latest papers is available at
this https URL

本文旨在追踪和总结 MLLM 的最新进展，包括 MLLM 的公式，技术和应用，以及现有的挑战和有前途的研究方向。