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Despite progress across a broad range of applications, Transformers havelimited success in systematic generalization. The situation is especiallyfrustrating in the case of algorithmic tasks, where they often fail to findintuitive solutions that route relevant information to the right node/operationat the right time in the grid represented by Transformer columns. To facilitatethe learning of useful control flow, we propose two modifications to theTransformer architecture, copy gate and geometric attention. Our novel NeuralData Router (NDR) achieves 100% length generalization accuracy on the classiccompositional table lookup task, as well as near-perfect accuracy on the simplearithmetic task and a new variant of ListOps testing for generalization acrosscomputational depths. NDR's attention and gating patterns tend to beinterpretable as an intuitive form of neural routing. Our code is public.

The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization