AI BriefingRecursive Transformers for Compositional Generalization
Key point
A study was published that improves compositional generalization performance by recursively leveraging the model's depth instead of increasing computation time.
Details
Depth-Recurrent Transformers proposes a structure designed to let the model think more deeply by recursively using layers, instead of increasing the model's computation time.
This research focuses in particular on solving the Compositional Generalization problem. Unlike existing Transformer models that tried to improve performance by increasing the number of layers, it efficiently strengthens reasoning ability by having the same layer repeatedly traversed.
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