Summary of Discrete Flow Matching, by Itai Gat et al.
Discrete Flow Matching
by Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman
First submitted to arxiv on: 22 Jul 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 research introduces Discrete Flow Matching (DFM), a novel generative paradigm specifically designed for producing discrete data such as language. DFM builds upon the success of continuous variable-based paradigms like Flow Matching and diffusion models, but adapts them to high-dimensional discrete data. The key contributions include: (i) a general family of probability paths interpolating between source and target distributions; (ii) a generic formula for sampling from these probability paths using learned posteriors; (iii) improved generative perplexity through specific schedulers; and (iv) large-scale models reaching state-of-the-art performance on HumanEval and 1-shot MBPP coding benchmarks. This non-autoregressive approach significantly closes the gap between autoregressive models and discrete flow models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to generate language, called Discrete Flow Matching. It’s like a special kind of computer program that can create text that looks real. The researchers took ideas from other programs that work well with images and videos, but adapted them for working with words. They made some important changes to make it better, and they tested it on big datasets. The results are really good – the generated text is very similar to what humans would write. This new approach is different because it doesn’t look ahead at what’s coming next, unlike other programs that do this. It’s like a big step forward in making computers smarter. |
Keywords
» Artificial intelligence » 1 shot » Autoregressive » Diffusion » Perplexity » Probability
