Summary of Retrieval-augmented Personalization For Multimodal Large Language Models, by Haoran Hao et al.
Retrieval-Augmented Personalization for Multimodal Large Language Models
by Haoran Hao, Jiaming Han, Changsheng Li, Yu-Feng Li, Xiangyu Yue
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The RAP framework for multimodal large language models (LLMs) personalization enhances their capabilities as general assistants. The approach involves three steps: remembering user-related information in a key-value database, retrieving relevant data using a multimodal retriever, and generating personalized responses based on the input query and retrieved concepts. This allows real-time concept editing via updating the external database. To improve generation quality, a pipeline for data collection is designed, creating a specialized dataset for personalized training of MLLMs. The trained models demonstrate flexibility and high generation quality across various tasks like image captioning, question answering, and visual recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The RAP framework helps large language models become better assistants by making them more personal. It does this in three steps: remember important user details, find the right information to use, and generate a personalized response. This allows the model to be updated in real-time to reflect changes in the user’s preferences. The framework also includes a way to collect more data and train the models specifically for each person. This makes the models very good at generating responses that are relevant to the user. |
Keywords
» Artificial intelligence » Image captioning » Question answering
