Knowledge Distillation via Random Layer Permutation
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Solution Overview
Problem
Existing knowledge distillation methods for pre-trained language models in customer relations management, particularly in contact centers, are inefficient and resource-intensive, making it challenging to deploy these models effectively.
Innovation Solution
A method for creating a student model from a teacher model by randomly selecting and removing layers from the teacher model, training the student model on a subset of the training data, and evaluating its performance using a predetermined criterion to identify the best-performing student model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge distillation is performed using traditional methods for pre-trained language models, then the model can capture complex linguistic patterns, but the computational resources and time required become excessively high
Solution Approach 1:
The patent segments the distillation process into multiple iterations, where in each iteration a subset of student models is trained with different layer configurations. This allows parallel processing and evaluation of multiple model variants simultaneously, reducing overall distillation time while maintaining performance through selective iteration based on performance thresholds.
Solution Approach 2:
The patent applies partial action by training student models with only a subset of the teacher model's layers initially, then selectively adding layers in subsequent iterations based on performance needs. This avoids the excessive computational action of training full-layer models from scratch, reducing time and resource consumption while achieving comparable performance.
2Device complexity
If the student model uses fewer layers to reduce complexity, then resource utilization improves, but model performance may deteriorate
Solution Approach 1:
The patent makes the model architecture dynamic by allowing the number and configuration of layers to vary across different student models and iterations. Student models start with fewer layers for lower complexity, then layers are dynamically added in subsequent iterations based on performance evaluation, enabling the system to adapt complexity to performance requirements.
Solution Approach 2:
The patent changes the structural parameter of layer configuration across different student models. By systematically varying the number and arrangement of layers in candidate student models, the patent identifies optimal parameter configurations that balance complexity reduction with performance maintenance, selecting the best-performing configuration for deployment.
3Reliability
If multiple student models are trained with different layer permutations, then the best model can be selected based on performance, but the training process becomes more resource-intensive
Solution Approach 1:
The patent applies partial action by training and evaluating only a limited number of student model iterations with varying layer configurations, rather than exhaustively training all possible permutations. The process stops when performance thresholds are met or a maximum iteration count is reached, reducing computational resource consumption while still achieving reliable model selection.
Solution Approach 2:
The patent implements self-service through automated performance evaluation and selection mechanisms. The system automatically trains candidate student models, evaluates their performance on validation data, compares metrics, and selects the best-performing model without manual intervention, reducing the operational overhead and resource management burden.
Data Source
AI summary
A method for creating a student model from a teacher model for knowledge distillation. The method may include: providing the teacher model trained on a first training dataset; generating candidate student models, wherein each of the candidate student models is a model having a unique permutation of layers derived by randomly selecting one or more layers of the plurality of layers of the teacher model for removing; generating a second training dataset; for each of the candidate student models: providing the second training dataset as inputs; recording outputs generated; and based on the recorded outputs, evaluating a performance according to a predetermined model evaluation criterion; determining which of the candidate student models performed best among the candidate student models based on the predetermined model evaluation criterion; identifying a preferred candidate student model.