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Computer Science > Computation and Language

arXiv:2303.09752 (cs)
[Submitted on 17 Mar 2023]

Title:CoLT5: Faster Long-Range Transformers with Conditional
Computation

Authors:Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontanon,
Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James
Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai
Download a PDF of the paper titled CoLT5: Faster Long-Range
Transformers with Conditional Computation, by Joshua Ainslie and 11
other authors
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    Abstract: Many natural language processing tasks benefit from
    long inputs, but processing long documents with Transformers is
    expensive -- not only due to quadratic attention complexity but
    also from applying feedforward and projection layers to every
    token. However, not all tokens are equally important, especially
    for longer documents. We propose CoLT5, a long-input Transformer
    model that builds on this intuition by employing conditional
    computation, devoting more resources to important tokens in both
    feedforward and attention layers. We show that CoLT5 achieves
    stronger performance than LongT5 with much faster training and
    inference, achieving SOTA on the long-input SCROLLS benchmark.
    Moreover, CoLT5 can effectively and tractably make use of
    extremely long inputs, showing strong gains up to 64k input
    length.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2303.09752 [cs.CL]
          (or arXiv:2303.09752v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2303.09752
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Joshua Ainslie [view email]
[v1] Fri, 17 Mar 2023 03:28:17 UTC (7,558 KB)
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    Download a PDF of the paper titled CoLT5: Faster Long-Range
    Transformers with Conditional Computation, by Joshua Ainslie and
    11 other authors
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