Summary of Nvlm: Open Frontier-class Multimodal Llms, by Wenliang Dai et al.
NVLM: Open Frontier-Class Multimodal LLMs
by Wenliang Dai, Nayeon Lee, Boxin Wang, Zhuolin Yang, Zihan Liu, Jon Barker, Tuomas Rintamaki, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 This paper introduces NVLM 1.0, a family of multimodal large language models that outperform existing proprietary and open-access models on vision-language tasks. The novel architecture combines decoder-only and cross-attention-based approaches to enhance both training efficiency and multimodal reasoning capabilities. The authors also introduce a tile-tagging design for dynamic high-resolution images, which improves performance on OCR-related tasks. The paper highlights the importance of dataset quality and task diversity during pretraining, even with large models. Furthermore, NVLM 1.0 excels in vision-language tasks while maintaining text-only performance compared to its LLM backbone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new kind of AI model that can understand both words and images really well. It’s called NVLM 1.0, and it’s better than other similar models at doing things like recognizing what’s in pictures or understanding what someone is saying based on what they’re looking at. The researchers came up with a new way to design these kinds of AI models that makes them work better. They also found ways to make the models understand text and images even better by using more data and training them on specific tasks. This could be useful for things like helping people who are blind or making computers better at understanding what we’re saying. |
Keywords
» Artificial intelligence » Cross attention » Decoder » Pretraining
