Summary of Futurefill: Fast Generation From Convolutional Sequence Models, by Naman Agarwal et al.
FutureFill: Fast Generation from Convolutional Sequence Models
by Naman Agarwal, Xinyi Chen, Evan Dogariu, Vlad Feinberg, Daniel Suo, Peter Bartlett, Elad Hazan
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 FutureFill, a method that enhances efficient auto-regressive generation in sequence prediction models based on convolutional operators. The approach reduces the generation time requirement from quadratic to quasilinear relative to the context length, making it suitable for any sequence prediction algorithm. Additionally, FutureFill requires a prefill cache sized only by the number of tokens generated, which is smaller than the cache requirements for standard convolutional and attention-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to generate text quickly using special algorithms called sequence prediction models. The method, called FutureFill, makes it possible to generate text much faster than before. It also uses less memory than other methods that do this kind of thing. |
Keywords
» Artificial intelligence » Attention » Context length
