Papers › FNetAR: Mixing Tokens with Autoregressive Fourier Transforms

FNetAR: Mixing Tokens with Autoregressive Fourier Transforms

22 Jul 2021arXiv:2107.10932archive 2025-07-28

Tim Lou, Michael Park, Mohammad Ramezanali, Vincent Tang

In this note we examine the autoregressive generalization of the FNet algorithm, in which self-attention layers from the standard Transformer architecture are substituted with a trivial sparse-uniformsampling procedure based on Fourier transforms. Using the Wikitext-103 benchmark, we demonstratethat FNetAR retains state-of-the-art performance (25.8 ppl) on the task of causal language modelingcompared to a Transformer-XL baseline (24.2 ppl) with only half the number self-attention layers,thus providing further evidence for the superfluity of deep neural networks with heavily compoundedattention mechanisms. The autoregressive Fourier transform could likely be used for parameterreduction on most Transformer-based time-series prediction models.

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