Multi-task Knowledge Distillation for Language Model Compression
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Solution Overview
Problem
Pre-trained language models for natural language processing are resource-intensive due to their large number of parameters, leading to high computational costs during inference, while maintaining comparable quality of output.
Innovation Solution
The implementation of a multi-task learning framework that employs knowledge distillation from a larger teacher model to a smaller student model, using shared layers and task-specific layers, allowing for the transfer of knowledge and reducing model size while maintaining performance and inference speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-trained language models use large number of parameters to achieve high accuracy, then natural language processing performance is improved, but computational costs and resource consumption increase
Solution Approach 1:
The patent creates a compressed student model that copies essential knowledge from the large teacher model through knowledge distillation. The student model replicates the teacher's linguistic capabilities using significantly fewer parameters (e.g., 125M vs 350M parameters in the example), achieving comparable NLP performance while reducing computational resource requirements.
Solution Approach 2:
The patent transforms the model parameter space by training a smaller student model to approximate the teacher model's behavior. Through careful parameter optimization and distillation, the student model achieves efficient parameter usage while maintaining performance, effectively changing the parameter-count-performance relationship.
2Measurement precision
If pre-trained language models use large number of parameters to achieve high accuracy, then natural language processing performance is improved, but inference speed decreases
Solution Approach 1:
The student model copies the essential computational patterns and linguistic representations from the teacher model, enabling fast inference. By capturing the distillation of knowledge in a compact form, the student model achieves both high performance and fast processing speeds, overcoming the traditional trade-off between model size and inference speed.
3Quantity of substance
If knowledge distillation is applied to reduce model size, then number of parameters is reduced, but model performance may deteriorate
Solution Approach 1:
The knowledge distillation process uses feedback from the teacher model's intermediate representations and output distributions to train the student model. This feedback mechanism ensures that the student model captures essential patterns and maintains performance quality while using fewer parameters.
Solution Approach 2:
The student model is designed to perform multiple NLP tasks universally by learning general linguistic patterns from the teacher model. This multi-functionality approach allows the compact student model to handle diverse NLP problems effectively without requiring task-specific large models.
Data Source
AI summary
Systems and methods are provided that employ knowledge distillation under a multi-task learning setting. In some embodiments, the systems and methods are implemented with a larger teacher model and a smaller student model, each of which comprise one or more shared layers and a plurality of task layers for performing multiple tasks. During training of the teacher model, its shared layers are initialized, and then the teacher model is multi-task refined. The teacher model predicts teacher logits. During training of the student model, its shared layers are initialized. Knowledge distillation is employed to transfer knowledge from the teacher model to the student model by the student model updating its shared layers and task layers, for example, according to the teacher logits of the teacher model. Other features are also provided.