Summary of Pin: a Knowledge-intensive Dataset For Paired and Interleaved Multimodal Documents, by Junjie Wang et al.
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
by Junjie Wang, Yin Zhang, Yatai Ji, Yuxiang Zhang, Chunyang Jiang, Yubo Wang, Kang Zhu, Zekun Wang, Tiezhen Wang, Wenhao Huang, Jie Fu, Bei Chen, Qunshu Lin, Minghao Liu, Ge Zhang, Wenhu Chen
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); 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 The paper introduces a novel dataset format called PIN (Paired and INterleaved multimodal documents) to enhance Large Multimodal Models’ capabilities in complex knowledge-driven tasks. The PIN format addresses perceptual and reasoning errors by combining markdown files and comprehensive images, enriching training data with a dense knowledge structure and versatile training strategies. The paper presents PIN-14M, an open-source dataset comprising 14 million samples derived from diverse sources, tailored to include complex web and scientific content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way of organizing information called PIN (Paired and INterleaved multimodal documents) to help big AI models do better at understanding complex things. They made this new format by combining text files with lots of pictures, so the AI model can learn more about how things are connected. |
