Randomized Positional Encodings Boost Length Generalization of
  Transformers
Anian Ruoss Grégoire Delétang Tim Genewein Jordi Grau-Moya Róbert Csordás Mehdi Bennani Shane Legg Joel Veness
Transformers have impressive generalization capabilities on tasks with a
fixed context length. However, they fail to generalize to sequences of
arbitrary length, even for seemingly simple tasks such as duplicating a string.
Moreover, simply training on longer sequences is inefficient due to the
quadratic computation complexity of the global attention mechanism. In this
work, we demonstrate that this failure mode is linked to positional encodings
being out-of-distribution for longer sequences (even for relative encodings)
and introduce a novel family of positional encodings that can overcome this
problem. Concretely, our randomized positional encoding scheme simulates the
positions of longer sequences and randomly selects an ordered subset to fit the
sequence's length. Our large-scale empirical evaluation of 6000 models across
15 algorithmic reasoning tasks shows that our method allows Transformers to
generalize to sequences of unseen length (increasing test accuracy by 12.0% on
average).