Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications. We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly. Furthermore, we discuss how such techniques can be combined or adapted when appropriate, highlighting their advantages and underlying rationale. We also provide a succinct summary of personalization tasks investigated in existing research, along with the evaluation metrics commonly used. Additionally, we summarize the datasets that are useful for benchmarking personalized MLLMs. Finally, we outline critical open challenges. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and advance the development of personalized multimodal large language models.

本研究针对个性化多模态大语言模型（MLLMs）存在的研究空白，提供了全面的综述，重点讨论其架构、训练方法和应用。我们提出了一种直观的分类法来归纳个性化MLLMs的技术，并指出这些技术的组合或调整策略，强调其优势及基本原理，旨在为相关研究和实践人员提供有价值的参考资料。

个性化多模态大语言模型：综述