Summary of Model Composition For Multimodal Large Language Models, by Chi Chen et al.
Model Composition for Multimodal Large Language Models
by Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a new paradigm for creating Multimodal Large Language Models (MLLMs) that understand inputs from various modalities. By composing existing MLLMs, the authors retain the modal understanding capabilities of each original model and create a versatile model that can process inputs from multiple modalities. The basic implementation, NaiveMC, demonstrates effectiveness, while DAMC addresses parameter interference issues. The paper also proposes MCUB, a benchmark for assessing multimodal understanding abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating special language models that can understand things like pictures and sounds too, not just words. Right now, making these models takes a lot of work and data. But the authors found a way to take existing models and combine them to create a new one that’s even better! They tested this idea on some tasks and it worked really well. The goal is to make language models that can understand lots of different things, not just words. |
