Related papers: Retrieval-Augmented Personalization for Multimodal Large Language Models

Retrieval-Augmented Personalization for Multimodal Large Language Models

URL: http://arxiv.org/abs/2410.13360v2
Date: Mon, 18 Nov 2024 15:35:14 GMT
Title: Retrieval-Augmented Personalization for Multimodal Large Language Models
Authors: Haoran Hao, Jiaming Han, Changsheng Li, Yu-Feng Li, Xiangyu Yue,
Abstract summary: We introduce the Retrieval Augmented Personalization (RAP) framework for MLLMs' personalization. RAP allows real-time concept editing via updating the external database. RAP-MLLMs can generalize to infinite visual concepts without additional finetuning.
Score: 53.304699445700926
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Abstract: The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this paper, we introduce the Retrieval Augmented Personalization (RAP) framework for MLLMs' personalization. Starting from a general MLLM, we turn it into a personalized assistant in three steps. (a) Remember: We design a key-value database to store user-related information, e.g., user's name, avatar and other attributes. (b) Retrieve: When the user initiates a conversation, RAP will retrieve relevant information from the database using a multimodal retriever. (c) Generate: The input query and retrieved concepts' information are fed into MLLMs to generate personalized, knowledge-augmented responses. Unlike previous methods, RAP allows real-time concept editing via updating the external database. To further improve generation quality and alignment with user-specific information, we design a pipeline for data collection and create a specialized dataset for personalized training of MLLMs. Based on the dataset, we train a series of MLLMs as personalized multimodal assistants. By pretraining on large-scale dataset, RAP-MLLMs can generalize to infinite visual concepts without additional finetuning. Our models demonstrate outstanding flexibility and generation quality across a variety of tasks, such as personalized image captioning, question answering and visual recognition. The code, data and models are available at https://github.com/Hoar012/RAP-MLLM.