Hierarchical Transformer Attention for Structured Token Processing
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Solution Overview
Problem
Conventional machine learning models, particularly transformers, struggle to effectively process hierarchical data structures, lacking visibility and understanding of broader hierarchical structures inherent in data, leading to suboptimal performance.
Innovation Solution
Implementing a hierarchical attention mechanism within transformer architectures, using attention masking at each level of the hierarchy and employing multiple attention heads to process data hierarchically, thereby enhancing model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional transformer architectures process data without hierarchical attention, then the model structure remains simple, but the model cannot effectively capture hierarchical data structures and achieves suboptimal performance
Solution Approach 1:
The patent segments the data processing into multiple hierarchical levels (e.g., token level, word level, sentence level) and applies separate attention mechanisms at each level. This segmentation allows the model to capture hierarchical structures by processing data at different granularities simultaneously, resolving the contradiction between simple structure and effective hierarchical data capture.
Solution Approach 2:
The patent implements nested attention mechanisms where lower-level attention operations are nested within higher-level attention operations. Each level of attention mechanism processes its own tokens while incorporating information from lower levels, creating a nested structure that enables the model to capture hierarchical relationships without requiring a completely complex new architecture.
2Manufacturing precision
If hierarchical attention mechanism processes all tokens at each level, then computational completeness is improved, but computational efficiency decreases
Solution Approach 1:
The patent applies partial attention by selectively processing only relevant tokens at each hierarchical level rather than all tokens uniformly. The attention mechanism identifies and processes only the necessary subset of tokens for each level, achieving computational completeness for the hierarchical structure while improving efficiency by avoiding redundant processing of all tokens at every level.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a set of tokens input to a hierarchical attention mechanism is accessed, where the set of tokens corresponds to a model input having a data hierarchy comprising a plurality of levels. A first attention output is generated based on processing a first partition of tokens, from the set of tokens, using a first masked attention operation, where the first partition of token corresponds to a first level of the plurality of levels. A second attention output is generated based on processing a second partition of tokens, from the set of tokens, corresponding to a first element at a second level of the plurality of levels using a second masked attention operation. An aggregated attention output is generated based on the first attention output and the second attention output.