Summary of Uni-moe: Scaling Unified Multimodal Llms with Mixture Of Experts, by Yunxin Li et al.
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by Yunxin Li, Shenyuan Jiang, Baotian Hu, Longyue Wang, Wanqi Zhong, Wenhan Luo, Lin Ma, Min Zhang
First submitted to arxiv on: 18 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); 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 This pioneering work presents Uni-MoE, a unified Multimodal Large Language Model (MLLM) architecture that can efficiently handle a wide range of modalities. By employing the Mixture of Experts (MoE) architecture and modality-specific encoders with connectors for a unified multimodal representation, Uni-MoE enables scalable and efficient training and inference. The authors also propose a progressive training strategy to enhance multi-expert collaboration and generalization. They evaluate Uni-MoE on various multimodal datasets and demonstrate its ability to significantly reduce performance bias in handling mixed multimodal datasets. This work highlights the potential of MoE frameworks in advancing MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of AI model that can understand many types of information at once, like pictures and words. The model is called Uni-MoE and it’s special because it can handle lots of different kinds of data. To make it work well, the creators came up with a plan to train the model in a way that makes all the different parts work together. They tested it on many types of information and found that it did really well! This is important because it could help us use AI models to do lots of useful things, like understand what’s going on in pictures or videos. |
Keywords
» Artificial intelligence » Generalization » Inference » Large language model » Mixture of experts
