Summary of World Model on Million-length Video and Language with Blockwise Ringattention, by Hao Liu et al.
World Model on Million-Length Video And Language With Blockwise RingAttention
by Hao Liu, Wilson Yan, Matei Zaharia, Pieter Abbeel
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 new paper tackles the challenge of scaling sequence models to understand long contexts, a crucial step towards developing generally intelligent AI that can process vast amounts of data. The authors provide a comprehensive guide on producing 1M context language models and video-language models, setting new benchmarks in language retrieval and video understanding. They detail their data curation process, progressive extension from 4K to 1M tokens, and share an efficient open-source implementation for training on long sequences. This paper also opens-source a family of 7B parameter models capable of processing documents and videos exceeding 1M tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has made progress in developing AI that can understand very long pieces of text or video. They’ve created special language models that can process millions of words or characters at once. This is important because it could help computers learn more about the world and make them smarter. The authors explain how they collected and organized their data, and then used that data to train powerful AI models. These models are very good at understanding text or video up to 1 million characters long. |
