Neural Network Output Insertion for Parallel Generation
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Solution Overview
Problem
Auto-regressive neural networks are limited in their ability to generate outputs, as they require new outputs to be added only at the end of the input sequence, restricting flexibility and increasing computational resources required for generation.
Innovation Solution
The system allows new outputs to be added at arbitrary positions within the input sequence, enabling multiple outputs to be generated in parallel at a single time step, using a decoder neural network that selects insertion locations and outputs based on score distributions to improve output quality and reduce generation time steps and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new outputs are added only at the end of the input sequence in auto-regressive models, then the model structure remains simple, but the flexibility of output positioning is restricted and the number of generation time steps increases
Solution Approach 1:
The patent segments the output generation process into multiple parallel operations at each time step. Instead of generating one output at a time at the end of the sequence, the system divides the generation into multiple insertion operations that can occur simultaneously at different positions within the sequence, thereby increasing flexibility without proportionally increasing complexity
Solution Approach 2:
The patent introduces a new dimension to the generation process by allowing outputs to be inserted at arbitrary positions within the sequence rather than only at the end. This transforms the generation from a one-dimensional sequential process to a multi-dimensional process where position selection becomes an additional degree of freedom
2Productivity
If multiple outputs are added at each time step, then the number of generation time steps is reduced, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating score distributions for multiple potential output positions at each time step. This allows the system to identify and select multiple insertion positions in advance, enabling parallel generation without requiring excessive computational resources during the actual generation process
Solution Approach 2:
The patent changes key parameters of the generation process, including allowing multiple outputs per time step and enabling arbitrary position selection. These parameter changes are managed through score distributions that guide the selection process, balancing the increased productivity with controlled computational resource usage
3Reliability
If arbitrary positions are allowed for output insertion, then output generation quality improves, but the complexity of selecting insertion locations increases
Solution Approach 1:
The patent implements feedback mechanisms through score distributions that evaluate and rank potential insertion positions. The system uses these scores to guide the selection process, providing feedback that helps manage the complexity of position selection while maintaining high output generation quality through informed decision-making
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating network outputs using insertion operations.