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Preventing the performance decay of Transformers on inputs longer than thoseused for training has been an important challenge in extending the contextlength of these models. Though the Transformer architecture has fundamentallyno limits on the input sequence lengths it can process, the choice of positionencoding used during training can limit the performance of these models onlonger inputs. We propose a novel functional relative position encoding withprogressive interpolation, FIRE, to improve Transformer generalization tolonger contexts. We theoretically prove that this can represent some of thepopular relative position encodings, such as T5's RPE, Alibi, and Kerple. Wenext empirically show that FIRE models have better generalization to longercontexts on both zero-shot language modeling and long text benchmarks.

Functional Interpolation for Relative Positions Improves Long Context Transformers