Summary of Mm1: Methods, Analysis & Insights From Multimodal Llm Pre-training, by Brandon Mckinzie et al.
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
by Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug Kang, Ankur Jain, Hongyu Hè, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang, Jianyu Wang, Chong Wang, Nan Du, Tao Lei, Sam Wiseman, Guoli Yin, Mark Lee, Zirui Wang, Ruoming Pang, Peter Grasch, Alexander Toshev, Yinfei Yang
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 A novel approach to building performant Multimodal Large Language Models (MLLMs) is proposed, focusing on the importance of architecture components and data choices. By comprehensively ablating image encoders, vision language connectors, and pre-training data options, crucial design lessons are identified. For instance, a mix of image-caption, interleaved image-text, and text-only data is found to be essential for achieving state-of-the-art few-shot results across multiple benchmarks. Additionally, the image encoder’s resolution and token count have significant impacts, while the vision-language connector design is relatively unimportant. By scaling up this recipe, a family of multimodal models (MM1) is built, featuring dense models and mixture-of-experts variants that outperform existing models in pre-training metrics and achieve competitive performance after fine-tuning on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal Large Language Models are being developed to understand and generate text and images together. To do this well, the right architecture and data are crucial. This paper figures out which parts of a model are most important for getting good results. It shows that using a mix of different types of data, like images with captions and just plain text, is key to achieving state-of-the-art performance. The study also finds that the resolution and amount of information used when processing images matters, but how these images are connected to language isn’t as important. By building bigger models that use this knowledge, the authors create a family of multimodal models that perform well on various tasks. |
Keywords
* Artificial intelligence * Encoder * Few shot * Fine tuning * Mixture of experts * Token
