Knowledge Distillation Layer Pruning for NLP Model Deployment
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Solution Overview
Problem
Existing pre-trained language models in customer relations management are resource-intensive and cumbersome to deploy due to their large size and complexity, leading to performance bottlenecks in cloud computing environments.
Innovation Solution
A method for knowledge distillation that involves selecting specific layers from a teacher model to be kept or discarded in configuring a student model, using weight similarity criteria such as Euclidean distance or Bayesian Information Criterion to optimize the student model's architecture, thereby reducing complexity while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained language models are used in customer relations management, then model accuracy and performance are improved, but resource consumption and deployment complexity increase
Solution Approach 1:
The patent segments the pre-trained language model into distinct components: a teacher model (full-size, high accuracy) and a student model (distilled, lower complexity). The knowledge distillation process transfers essential patterns from the teacher to the student model, allowing the student to handle routine tasks with reduced resource requirements while maintaining acceptable accuracy levels.
Solution Approach 2:
The patent extracts critical knowledge and patterns from the large pre-trained model and transfers them to a smaller student model. This extraction process involves comparing outputs between teacher and student models on validation data, identifying discrepancies, and iteratively refining the student model to capture essential decision-making patterns without copying the entire original model.
2Reliability
If pre-trained language models are used in customer relations management, then model accuracy and performance are improved, but computational resources and processing power increase
Solution Approach 1:
The patent creates a simplified copy (student model) that mimics the behavior of the original pre-trained model (teacher model). The student model is trained to reproduce the teacher model's outputs on various tasks, capturing essential patterns while using significantly fewer computational resources during inference and deployment.
3Device complexity
If the student model architecture is optimized using weight similarity criteria, then model complexity is reduced, but training time and computational overhead increase
Solution Approach 1:
The patent performs preliminary actions by first training the student model on a subset of tasks, then using weight similarity criteria to identify and remove redundant layers. This iterative process of training, evaluating, and pruning is performed in advance to create an optimized model architecture before final deployment, reducing complexity while managing training time through systematic elimination of unnecessary computations.
Data Source
AI summary
A method for creating a student model from a teacher model for knowledge distillation. The method including: providing a first model; using a first instance of the first model to create the teacher model by training the first instance of the first model on a training dataset; using a second instance of the first model to create the student model by training the second instance of the first model on a subset of the training dataset; identifying corresponding layers in the teacher model and the student model; for each of the corresponding layers, computing a weight similarity criterion; ranking the corresponding layers according to the weight similarity criterion; selecting, based on the ranking, one or more of the corresponding layers for designation as one or more discard layers; removing from the student model the one or more discard layers.