Memory-Augmented Self-Attention for Long Sequence Processing
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Solution Overview
Problem
Existing neural networks face limitations in processing long input sequences due to high computational and memory complexity, making it infeasible to apply self-attention across entire sequences, especially for tasks like processing books or long documents, as they require quadratic growth with the number of elements, constraining them to small problem domains.
Innovation Solution
The system segments input sequences into multiple subsequences and applies self-attention at each stage, using a memory to store key-value pairs from previous stages, allowing attention across distant elements and dynamically selecting relevant previous elements for attention, thereby reducing computational complexity and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If self-attention is applied across the entire input sequence, then the neural network can model long-term dependencies, but the computational and memory complexity grows quadratically with the sequence length
Solution Approach 1:
The input sequence is divided into multiple segments or blocks. Self-attention is applied within each block rather than across the entire sequence. This segmentation reduces the computational complexity from O(n²) for the full sequence to O(m² × k) where m is the block size and k is the number of blocks, making long sequence processing feasible while preserving attention mechanisms for capturing dependencies within reasonable computational limits.
Solution Approach 2:
The patent introduces a temporal dimension by processing sequences in multiple passes or stages. Instead of applying self-attention once across the entire sequence, the system applies it repeatedly across different passes, allowing the model to capture long-term dependencies by accumulating attention information over multiple temporal steps, effectively transforming the problem from a single high-dimensional operation to multiple lower-dimensional operations.
2Adaptability or versatility
If the input sequence length is increased to process long documents or books, then the neural network can handle larger problem domains, but the quadratic growth in computational requirements makes it infeasible
Solution Approach 1:
Long documents and books are divided into smaller manageable segments or chapters. The neural network processes each segment separately using self-attention, then combines the results. This allows the system to handle arbitrarily long inputs by breaking them into pieces that fit within computational memory constraints, thereby expanding the problem domain without requiring quadratic computational resources for the entire document.
Solution Approach 2:
The system performs preliminary processing of long sequences by segmenting them before applying self-attention. By pre-dividing the input into manageable blocks and processing them in stages, the system prepares the data in a way that reduces subsequent computational burden, enabling handling of large problem domains like entire books or long journal articles without overwhelming computational requirements.
3Loss of information
If attention is applied to all preceding elements for each element in the sequence, then complete context is captured, but the computational complexity becomes prohibitive for long sequences
Solution Approach 1:
Instead of computing attention between all pairs of elements in a long sequence (which would be O(n²)), the sequence is segmented into blocks where attention is only computed within each block. This local attention approach captures contextual information within reasonable computational limits while reducing the overall complexity from quadratic to linear or near-linear with respect to sequence length.
Solution Approach 2:
The system applies attention selectively to relevant portions of the sequence rather than all preceding elements. By using techniques like local attention windows, sparse attention patterns, or attention masking, the system captures the most important contextual information without computing attention for every possible element pair, thereby maintaining adequate context capture while reducing computational complexity to manageable levels.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more augmented neural network blocks. Each augmented network block applies two different attention mechanisms: a first attention mechanism over representations of the current network input and a second attention mechanism that is augmented with a memory that stores outputs previously generated by the first attention mechanism.