Summary of Eagle: Exploring the Design Space For Multimodal Llms with Mixture Of Encoders, by Min Shi et al.
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
by Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, Yilin Zhao, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, Bryan Catanzaro, Andrew Tao, Jan Kautz, Zhiding Yu, Guilin Liu
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 paper explores the ability of multimodal large language models (MLLMs) to accurately interpret complex visual information. Recent advancements in this area have shown that enhanced visual perception can significantly reduce hallucinations and improve performance on resolution-sensitive tasks, such as optical character recognition and document analysis. The study provides a systematic comparison and ablation study of various design choices for MLLMs using a mixture of vision encoders and resolutions. The findings reveal several underlying principles common to existing strategies, leading to a streamlined yet effective design approach. The Eagle family of MLLMs outperforms other leading open-source models on major benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computers can better understand pictures and written words. It shows that making computers see more clearly helps them do tasks like reading books and recognizing handwriting. The study looks at different ways to combine computer vision with language processing to make computers smarter. The results show that a simple way of combining visual information works just as well as more complicated methods. |
