Universal Transformer With Adaptive Depth for Long-Sequence Generalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Self-attentive feed-forward sequence models like the Transformer struggle to generalize in tasks involving copying strings or simple logical inference when the string or formula lengths exceed those observed at training time, failing to handle these tasks with ease compared to recurrent neural networks.
Innovation Solution
The Universal Transformer employs a recurrent structure in depth, using self-attention to combine information across sequence parts, with parameter tying across layers and an adaptive computation time mechanism, allowing it to dynamically adjust computational steps based on input sequence complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the Transformer model uses self-attentive feed-forward structure with fixed depth, then parallelizability is improved, but generalization capability deteriorates when sequence lengths exceed training time observations
Solution Approach 1:
The patent applies dynamics by making the depth of the Transformer model variable rather than fixed. The model can dynamically adjust the number of recurrent steps (depth) based on the complexity of the input sequence, allowing it to generalize better to sequences of varying lengths while maintaining parallelizability through self-attention mechanisms.
Solution Approach 2:
The patent implements universality by creating a recurrent architecture that can handle multiple sequence lengths and complexities using the same parameter set. The tied parameters across layers enable the model to function effectively as a universal sequence processor that adapts to different task requirements without retraining.
2Adaptability or versatility
If the Transformer model increases depth to improve generalization, then adaptability is improved, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by tying the same parameters across all recurrent steps (layers) rather than having unique parameters for each layer. This parameter sharing reduces the total number of parameters and computational complexity while maintaining the ability to increase depth for better generalization when needed.
3Productivity
If the model uses fixed number of computational steps, then training efficiency is improved, but ability to handle varying sequence complexity deteriorates
Solution Approach 1:
The patent applies dynamics by enabling the model to vary the number of computational steps (depth) based on input sequence complexity. During training and inference, the model can adaptively choose how many recurrent steps to perform, balancing training efficiency with the ability to handle complex sequences that require more processing steps.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a sequence-to-sequence model that is recurrent in depth while employing self-attention to combine information from different parts of sequences.