Sequence Generation Neural Networks With Explicit Alignment Tokens
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Solution Overview
Problem
Existing machine learning models lack explicit alignment mechanisms between input and output, leading to inconsistencies and inefficiencies in generating high-quality outputs, particularly in natural language processing tasks.
Innovation Solution
A system that generates a combined output sequence of tokens including alignment tokens, which explicitly encode the relationship between input and output elements, trained using ground truth alignment tokens to enhance alignment during inference time, reducing the need for additional post-processing models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If explicit alignment mechanisms are added to sequence generation neural networks, then alignment quality and output consistency improve, but device complexity and training difficulty increase
Solution Approach 1:
The patent segments the output sequence into distinct components: regular output tokens and alignment tokens. Each token type serves a specific function - output tokens carry the primary information while alignment tokens encode the correspondence relationships. This segmentation allows the model to explicitly track alignment without fundamentally changing the overall architecture, resolving the contradiction between alignment quality and model complexity.
Solution Approach 2:
The patent introduces alignment tokens as intermediary elements that mediate between the input sequence and output sequence. These tokens act as a bridge, explicitly encoding the alignment relationships without requiring direct modification of the core generation mechanism. The alignment tokens serve as intermediaries that carry alignment information through the model, improving alignment quality while maintaining architectural simplicity.
2Loss of time
If alignment tokens are generated during inference, then alignment information becomes available without post-processing, but computational resources and processing time increase
Solution Approach 1:
The patent performs alignment encoding during the main generation process rather than as a separate post-processing step. By generating alignment tokens concurrently with output tokens during inference, the model performs the alignment encoding action preliminarily, eliminating the need for subsequent post-processing operations. This approach trades some computational resources during generation for significant time savings by eliminating separate alignment processing steps.
3Measurement precision
If the neural network is trained with ground truth alignment tokens, then alignment accuracy improves, but training data requirements and training complexity increase
Solution Approach 1:
The patent uses ground truth alignment tokens as target copies during training. The model learns to generate alignment tokens that copy or match the predefined ground truth alignment relationships. This copying approach allows the model to learn accurate alignment patterns from labeled data without requiring complex training algorithms, improving alignment accuracy while keeping training methodology relatively simple.
4Loss of information
If combined output sequence with alignment tokens is generated, then transparency and accountability improve, but output sequence length and processing overhead increase
Solution Approach 1:
The patent applies local quality by making different parts of the output sequence serve different functions. Regular output tokens carry the primary information content while alignment tokens provide metadata about correspondence relationships. This local differentiation allows the system to preserve complete alignment information in the output sequence without requiring uniform enhancement of all token types, minimizing the overall impact of increased sequence length while maximizing the value of alignment information.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output with a corresponding alignment that defines how the output relates to the model input. In one aspect, a system comprises receiving a model input, processing the model input to generate an input sequence of input tokens that represent the model input, generating, by processing the input sequence of input tokens using a sequence generation neural network, a combined output sequence of tokens comprising alignment tokens and output tokens, wherein each alignment token encodes an alignment between at least one of the input tokens and one or more of the output tokens according to an alignment mapping encoding, and generating an output comprising one or more output elements by decoding at least the output tokens.