$\infty$-former: Infinite Memory Transformer

TL;DR

The $ abla$-former introduces an unbounded long-term memory for transformers using continuous-space attention, enabling modeling of arbitrarily long contexts with fixed computational cost, demonstrated across various tasks.

Contribution

It presents the $ abla$-former, a transformer variant with unbounded memory and fixed complexity, using continuous attention to handle arbitrarily long sequences.

Findings

Successfully models long sequences in synthetic and real tasks.

Maintains fixed computational complexity regardless of context length.

Demonstrates improved long-term information retention.

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.