Transformer Model Attention Updates for Relative Event Timing
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Solution Overview
Problem
Transformer models lack the ability to account for relative timing between events, leading to inaccurate predictions in contexts outside natural language processing, such as predicting human behavior.
Innovation Solution
Update attention values in transformer models by subtracting respective time differences between events, allowing a single calculation to be applied across all layers to account for relative timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer models use standard attention mechanisms without timing adjustments, then they can process sequences efficiently, but they cannot accurately capture relative timing between events
Solution Approach 1:
The patent segments the attention mechanism into two distinct components: standard attention weights for sequence processing and timing adjustments for temporal relationships. By separating these functions, the model can maintain efficient sequence processing while independently optimizing for timing accuracy through the addition of time difference calculations between events.
Solution Approach 2:
The patent introduces timing adjustments as an intermediary element that mediates between the standard attention mechanism and the final attention scores. This intermediary layer computes time differences between events and modulates the attention weights accordingly, enabling the model to capture relative timing without fundamentally redesigning the entire attention architecture.
2Reliability
If static positional adjustments are applied at each layer, then relative timing information is incorporated, but the calculations must be re-computed for every layer increasing computational overhead
Solution Approach 1:
The patent performs timing adjustments at the input embedding level before the data enters the transformer layers. By pre-computing the timing adjustments and incorporating them into the embeddings upfront, the model avoids redundant calculations in each layer while ensuring that timing information is available throughout the entire transformation process, thus maintaining prediction accuracy without sacrificing computational efficiency.
3Measurement precision
If positional information is used to represent timing, then the model can process sequence positions, but the precision is insufficient to reflect actual relative timing between events
Solution Approach 1:
The patent changes the parameter representation from discrete positional indices to continuous time differences between events. Instead of using positional encodings that only indicate sequence order, the model computes actual time differences (e.g., time intervals) between events and uses these as adjustment factors, thereby transforming the timing information from coarse positional data to precise temporal measurements.
Data Source
AI summary
Methods and systems are described herein for updating transformer machine learning models to account for relative timing within data. The system may retrieve a transformer model trained based on events. The events may include a first event associated with a first time and second events associated with second times. The system may generate event embeddings for the events. The system may input, into the transformer model, the event embeddings to cause the transformer model to perform a transformation on the event embeddings. The system may determine respective time differences between the first time of the first event and each corresponding second time of the second events. The system may generate attention values by aggregating the transformation and the respective time differences. The system may then update the transformer model with the attention values.