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arXiv · 2109.00301

$\infty$-former: Infinite Memory Transformer

Pedro Henrique Martins·Zita Marinho·André F. T. Martins
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Abstract

Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length. While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information. In this paper, we propose the $\infty$-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the $\infty$-former's attention complexity becomes independent of the context length, trading off memory length with precision. In order to control where precision is more important, $\infty$-former maintains "sticky memories" being able to model arbitrarily long contexts while keeping the computation budget fixed. Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the $\infty$-former's ability to retain information from long sequences.

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Pedro Henrique Martins, Zita Marinho, André F. T. Martins. 2021-09-01. $\infty$-former: Infinite Memory Transformer. https://arxiv.org/abs/2109.00301

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