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TL;DR#
The increasing importance of multimodal large language models (MLLMs) is undeniable, as they excel at handling multiple data types and performing complex tasks. However, personalizing MLLMs to individual users poses unique challenges, including the need for individual-level data spanning various modalities and efficient handling of heterogeneous information with noise. Current research on personalized MLLMs focuses on different techniques like multimodal instructions and alignment, model fine-tuning, and diverse applications such as text and image generation and retrieval.
This paper addresses these challenges by proposing a new taxonomy for categorizing the personalization techniques used in MLLMs. It systematically categorizes existing methods based on their approach to personalization in various modalities (text, image, etc). The authors also provide a concise summary of personalization tasks, evaluation metrics, and useful datasets. By identifying open challenges in the field, the paper provides a valuable resource for researchers and practitioners seeking to advance the development of personalized MLLMs.
Key Takeaways#
Why does it matter?#
This paper is crucial for researchers in multimodal learning and personalization because it provides a comprehensive survey of the field, identifies key challenges, and proposes a novel taxonomy for personalizing multimodal large language models. It opens up new avenues for research, including developing better benchmark datasets and exploring more diverse modalities. The findings will directly impact the design, development, and evaluation of future personalized AI systems.
Visual Insights#
| Category | General Mechanism | Example Models and Methods |
|---|---|---|
| Personalized MLLM Text Generation | Instruction (Sec. 3.1) | CGSMP Yong et al. (2023), ModICT Li et al. (2024c) |
| (Section 3) | Alignment (Sec. 3.2) | MPDialog Agrawal et al. (2023), Athena 3.0 Fan et al. (2023) |
| Generation (Sec. 3.3) | Wu et al. (2024b), PTSCG Wang et al. (2024a) | |
| Fine-tuning (Sec. 3.4) | Wang et al. (2023), PVIT Pi et al. (2024) | |
| Personalized MLLM Image Generation | Instruction (Sec. 4.1) | MuDI Jang et al. (2024), Zhong et al. (2024) |
| (Section 4) | Alignment (Sec. 4.2) | λ-ECLIPSE Patel et al. (2024), MoMA Song et al. (2024) |
| Generation (Sec. 4.3) | Layout-and-Retouch Kim et al. (2024), Instantbooth Shi et al. (2024a) | |
| Fine-tuning (Sec. 4.4) | MS-Diffusion Wang et al. (2024d), UNIMO-G Li et al. (2024a) | |
| Personalized MLLM Recommendation | Instruction (Sec. 5.1) | InteraRec Karra and Tulabandhula (2024), X-Reflect Lyu et al. (2024b) |
| (Section 5) | Alignment (Sec. 5.2) | PMG Shen et al. (2024), MMREC Tian et al. (2024) |
| Generation (Sec. 5.3) | RA-Rec Yu et al. (2024), Wei et al. (2024a) | |
| Fine-tuning (Sec. 5.4) | GPT4Rec Zhang et al. (2024), MMSSL Wei et al. (2023) | |
| Personalized MLLM Retrieval | Instruction (Sec. 6.1) | ConCon-Chi Rosasco et al. (2024), Med-PMC Liu et al. (2024a) |
| (Section 6) | Alignment (Sec. 6.2) | AlignBot Chen et al. (2024c), Xu et al. (2024) |
| Generation (Sec. 6.3) | Ye et al. (2024a), Yo’LLaVA Nguyen et al. (2024) | |
| Fine-tuning (Sec. 6.4) | FedPAM Feng et al. (2024), VITR Gong et al. (2023) |
🔼 This table provides a categorized overview of the techniques used for personalization in multimodal large language models (MLLMs). It groups techniques based on four key categories: Text Generation, Image Generation, Recommendation, and Retrieval. Within each category, it lists example models and methods that utilize different mechanisms such as instruction, alignment, generation, and fine-tuning to achieve personalization. The table serves as a quick reference to understand the range of approaches used in personalizing MLLMs for different tasks and modalities.
read the caption
Table 1: Overview of Techniques for Personalized Multimodal Large Language Models (Sections 3-6).
Full paper#
