Summary of Scaling Law Hypothesis For Multimodal Model, by Qingyun Sun et al.
Scaling Law Hypothesis for Multimodal Model
by Qingyun Sun, Zhen Guo, PIN AI Team
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel framework for scaling multimodal models that process various types of data such as text, audio, images, and video. The authors develop a hypothesis that predicts model performance based on the efficiency of modality-specific compression and tokenization. This framework extends existing scaling laws from text-based decoder models to mixed-modality systems. By leveraging more training data across multiple modalities, the authors demonstrate that multimodal models can be reduced in size, making them deployable on resource-constrained devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make computer models that work with different types of information like text, sound, pictures, and videos. The idea is to use more training data from these different sources to make the model smaller and more efficient. This would allow it to run on devices with limited resources. |
Keywords
» Artificial intelligence » Decoder » Scaling laws » Tokenization
