Summary of Mint-1t: Scaling Open-source Multimodal Data by 10x: a Multimodal Dataset with One Trillion Tokens, By Anas Awadalla et al.
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
by Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Matt Jordan, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, Ran Xu, Yejin Choi, Ludwig Schmidt
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper introduces MINT-1T, a massive open-source multimodal interleaved dataset containing one trillion text tokens and 3.4 billion images, scaling up the existing datasets by 10 times. The dataset includes PDFs and ArXiv papers, providing a unique opportunity for training large multimodal models (LMMs). The authors demonstrate that LMMs trained on MINT-1T achieve comparable performance to those trained on OBELICS, the previous leading dataset. To facilitate further research, the data curation process is shared, and the dataset will be released. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a huge open-source dataset called MINT-1T that has lots of text and images mixed together. This kind of dataset is important for training special kinds of artificial intelligence models. The new dataset is really big, with over one trillion words of text and three billion pictures! It also includes some types of documents you might not normally think of as “data”, like PDFs and research papers. The researchers tested their model on this new dataset and found that it worked just as well as a similar model trained on an existing dataset. They’re sharing all the details so other scientists can use this data to make even better AI models. |
