Student Model Knowledge Distillation for Terminal Deployment
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Solution Overview
Problem
Existing pre-trained language models have a large number of parameters and long inference times, making them difficult to deploy on intelligent terminals due to size and runtime requirements.
Innovation Solution
A text processing model training method that involves obtaining training text, inputting it into both a teacher model and a student model, and training the student model based on sample data and prediction data to accurately learn the semantic representation capability of the teacher model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-trained language model with large number of parameters is used to achieve strong semantic representation capability, then the model accuracy is improved, but the model size and inference time increase making it difficult to deploy on intelligent terminals
Solution Approach 1:
The patent segments the knowledge transfer process into multiple stages: first training the student model to match teacher model outputs at the output layer, then progressively training intermediate layers to match teacher model intermediate outputs. This staged segmentation enables efficient knowledge transfer while maintaining model compactness for terminal deployment.
Solution Approach 2:
The patent introduces intermediate layer outputs as intermediary targets for knowledge distillation. By using intermediate layer predictions as additional training targets, the student model learns semantic representations at multiple levels, achieving strong semantic capability without requiring the full parameter count of the teacher model.
2Measurement precision
If a pre-trained language model with large number of parameters is used to achieve strong semantic representation capability, then the model accuracy is improved, but the inference time increases making it difficult to deploy on intelligent terminals
Solution Approach 1:
The patent segments the knowledge transfer process into multiple stages: first training the student model to match teacher model outputs at the output layer, then progressively training intermediate layers to match teacher model intermediate outputs. This staged segmentation enables efficient knowledge transfer while maintaining model compactness for terminal deployment.
Solution Approach 2:
The patent changes the training parameters by introducing intermediate layer loss functions in addition to output layer loss. This parameter change in the training objective enables the student model to learn efficient semantic representations that achieve high accuracy with fewer parameters and faster inference.
3Ease of manufacture
If knowledge distillation is performed only using output layer data, then the training process is simple, but the student model cannot accurately learn the semantic representation capability of the teacher model
Solution Approach 1:
The patent adds another dimension to knowledge distillation by incorporating intermediate layer outputs as additional training targets. This multi-dimensional approach (output layer + intermediate layers) enables the student model to capture semantic representations at multiple levels, significantly improving semantic understanding beyond what single-layer distillation achieves.
Solution Approach 2:
The patent introduces intermediate layer outputs as intermediary targets for knowledge distillation. By using intermediate layer predictions as additional training targets, the student model learns semantic representations at multiple levels, achieving strong semantic capability without requiring the full parameter count of the teacher model.
Data Source
AI summary
A text processing model training method, and a text processing method and apparatus in the natural language processing field in the artificial intelligence field are disclosed. The training method includes: obtaining training text; separately inputting the training text into a teacher model and a student model to obtain sample data output by the teacher model and prediction data output by the student model; the sample data includes a sample semantic feature and a sample label; the prediction data includes a prediction semantic feature and a prediction label; and the teacher model is a pre-trained language model used for text classification; and training a model parameter of the student model based on the sample data and the prediction data, to obtain a target student model. The method enables the student model to effectively perform knowledge transfer, thereby improving accuracy of a text processing result of the student model.