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Computer Science > Computation and Language

arXiv:2407.09298 (cs)
[Submitted on 12 Jul 2024]

Title:Transformer Layers as Painters

Authors:Qi Sun, Marc Pickett, Aakash Kumar Nain, Llion Jones
View a PDF of the paper titled Transformer Layers as Painters, by Qi
Sun and 3 other authors
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    Abstract:Despite their nearly universal adoption for large
    language models, the internal workings of transformers are not
    well understood. We aim to better understand the impact of
    removing or reorganizing information throughout the layers of a
    pretrained transformer. Such an understanding could both yield
    better usage of existing models as well as to make architectural
    improvements to produce new variants. We present a series of
    empirical studies on frozen models that show that the lower and
    final layers of pretrained transformers differ from middle
    layers, but that middle layers have a surprising amount of
    uniformity. We further show that some classes of problems have
    robustness to skipping layers, running the layers in an order
    different from how they were trained, or running the layers in
    parallel. Our observations suggest that even frozen pretrained
    models may gracefully trade accuracy for latency by skipping
    layers or running layers in parallel.

Comments: 15 pages total, including references and appendices
Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2407.09298 [cs.CL]
          (or arXiv:2407.09298v1 [cs.CL] for this version)

Submission history

From: Marc Pickett [view email]
[v1] Fri, 12 Jul 2024 14:31:05 UTC (1,397 KB)
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