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arXiv · 2310.16076

Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions

Kazuki Irie·Róbert Csordás·Jürgen Schmidhuber
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Abstract

Recent studies of the computational power of recurrent neural networks (RNNs) reveal a hierarchy of RNN architectures, given real-time and finite-precision assumptions. Here we study auto-regressive Transformers with linearised attention, a.k.a. linear Transformers (LTs) or Fast Weight Programmers (FWPs). LTs are special in the sense that they are equivalent to RNN-like sequence processors with a fixed-size state, while they can also be expressed as the now-popular self-attention networks. We show that many well-known results for the standard Transformer directly transfer to LTs/FWPs. Our formal language recognition experiments demonstrate how recently proposed FWP extensions such as recurrent FWPs and self-referential weight matrices successfully overcome certain limitations of the LT, e.g., allowing for generalisation on the parity problem. Our code is public.

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Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber. 2023-10-24. Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions. https://arxiv.org/abs/2310.16076

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