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Computer Science > Computation and Language

arXiv:2401.06104 (cs)
[Submitted on 11 Jan 2024]

Title:Transformers are Multi-State RNNs

Authors:Matanel Oren, Michael Hassid, Yossi Adi, Roy Schwartz
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by Matanel Oren and 3 other authors
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    Abstract:Transformers are considered conceptually different
    compared to the previous generation of state-of-the-art NLP
    models - recurrent neural networks (RNNs). In this work, we
    demonstrate that decoder-only transformers can in fact be
    conceptualized as infinite multi-state RNNs - an RNN variant with
    unlimited hidden state size. We further show that pretrained
    transformers can be converted into $\textit{finite}$ multi-state
    RNNs by fixing the size of their hidden state. We observe that
    several existing transformers cache compression techniques can be
    framed as such conversion policies, and introduce a novel policy,
    TOVA, which is simpler compared to these policies. Our
    experiments with several long range tasks indicate that TOVA
    outperforms all other baseline policies, while being nearly on
    par with the full (infinite) model, and using in some cases only
    $\frac{1}{8}$ of the original cache size. Our results indicate
    that transformer decoder LLMs often behave in practice as RNNs.
    They also lay out the option of mitigating one of their most
    painful computational bottlenecks - the size of their cache
    memory. We publicly release our code at this https URL.

Comments: preprint
Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2401.06104 [cs.CL]
          (or arXiv:2401.06104v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2401.06104
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Matanel Oren [view email]
[v1] Thu, 11 Jan 2024 18:35:26 UTC (803 KB)
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