Context-Augmented Transformer Attention for External Context Integration
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Solution Overview
Problem
Traditional transformer neural networks are resource-intensive and limited in their ability to utilize known contextual information beyond the input sequence, leading to inefficient training and inaccurate outputs, especially in non-natural-language-processing applications.
Innovation Solution
A contextually augmented transformer neural network that embeds contextual information within its attention mechanism, modifying the query or keys matrix to incorporate metadata or known contextual data, enhancing the self-attention mechanism's ability to leverage known contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional transformer neural networks are used to process sequence data, then the model can capture sequential patterns, but the computational resources and processing time required are excessive
Solution Approach 1:
The patent applies preliminary action by pre-processing the input sequence data to extract and organize relevant features before feeding them to the transformer model. This pre-processing step reduces the complexity and size of the input data, thereby decreasing the computational resources required during the actual model processing while maintaining output accuracy.
2Adaptability or versatility
If traditional transformer neural networks are used, then the model can process natural language sequences, but it cannot effectively utilize external contextual information
Solution Approach 1:
The patent merges external contextual information with the input sequence data by concatenating or integrating additional context vectors with the encoded sequence representations. This combination allows the model to simultaneously process both the original sequence patterns and external contextual cues, improving adaptability to different application scenarios while enhancing output accuracy through more comprehensive information processing.
3Adaptability or versatility
If the transformer architecture processes large numbers of parameters, then the model can learn complex patterns, but the training time and computational overhead increase significantly
Solution Approach 1:
The patent segments the transformer architecture into multiple specialized components or layers, each responsible for processing specific aspects of the data. This segmentation allows for more efficient parallel processing and reduces the computational overhead associated with processing large numbers of parameters as a monolithic structure, thereby decreasing training time while preserving the model's ability to learn complex patterns.
Data Source
AI summary
A contextually augmented transformer neural network is provided. Contextual data objects, such as subsequence contexts, token-level contexts, and token-to-token contexts, are embedded into an attention mechanism to provide the contextually augmented transformer neural network. The contextual data object is ingested with a sequence data object to improve attention mechanisms such as the query-key-value mechanism. The contextually augmented transformer neural network generates outputs based on input data including sequence data objects and contextual data objects. The contextual data object may be a different data type than the data included in the sequence data object and may not be a part of the sequence data object. The contextually augmented transformer neural network provides improved efficiency and accuracy in comparison to other neural networks.