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Computer Science > Computation and Language

arXiv:2305.01625 (cs)
[Submitted on 2 May 2023]

Title:Unlimiformer: Long-Range Transformers with Unlimited Length
Input

Authors:Amanda Bertsch, Uri Alon, Graham Neubig, Matthew R. Gormley
Download a PDF of the paper titled Unlimiformer: Long-Range
Transformers with Unlimited Length Input, by Amanda Bertsch and 3
other authors
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    Abstract: Transformer-based models typically have a predefined
    bound to their input length, because of their need to potentially
    attend to every token in the input. In this work, we propose
    Unlimiformer: a general approach that can wrap any existing
    pretrained encoder-decoder transformer, and offload the attention
    computation across all layers to a single $k$-nearest-neighbor
    index; this index can be kept on either the GPU or CPU memory and
    queried in sub-linear time. This way, we can index extremely long
    input sequences, while every attention head in every decoder
    layer retrieves its top-$k$ keys, instead of attending to every
    key. We demonstrate Unlimiformers's efficacy on several
    long-document and multi-document summarization benchmarks,
    showing that it can summarize even 350k token-long inputs from
    the BookSum dataset, without any input truncation at test time.
    Unlimiformer improves pretrained models such as BART and
    Longformer by extending them to unlimited inputs without
    additional learned weights and without modifying their code. We
    make our code and models publicly available at this https URL .

Comments: Preprint
Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2305.01625 [cs.CL]
          (or arXiv:2305.01625v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2305.01625
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Amanda Bertsch [view email]
[v1] Tue, 2 May 2023 17:35:08 UTC (7,045 KB)
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    Transformers with Unlimited Length Input, by Amanda Bertsch and 3
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