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Computer Science > Machine Learning

arXiv:2404.09562 (cs)
[Submitted on 15 Apr 2024]

Title:s-GPTs: A New Approach to Autoregressive Models

Authors:Arnaud Pannatier, Evann Courdier, Francois Fleuret
View a PDF of the paper titled {\sigma}-GPTs: A New Approach to
Autoregressive Models, by Arnaud Pannatier and 2 other authors
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    Abstract:Autoregressive models, such as the GPT family, use a
    fixed order, usually left-to-right, to generate sequences.
    However, this is not a necessity. In this paper, we challenge
    this assumption and show that by simply adding a positional
    encoding for the output, this order can be modulated on-the-fly
    per-sample which offers key advantageous properties. It allows
    for the sampling of and conditioning on arbitrary subsets of
    tokens, and it also allows sampling in one shot multiple tokens
    dynamically according to a rejection strategy, leading to a
    sub-linear number of model evaluations. We evaluate our method
    across various domains, including language modeling,
    path-solving, and aircraft vertical rate prediction, decreasing
    the number of steps required for generation by an order of
    magnitude.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2404.09562 [cs.LG]
          (or arXiv:2404.09562v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2404.09562
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Arnaud Pannatier [view email]
[v1] Mon, 15 Apr 2024 08:22:47 UTC (1,389 KB)
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