Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning
This work addresses the problem of computational expense in large transformer models for NLP practitioners, but it is incremental as it builds on existing knowledge distillation methods.
This project tackled improving knowledge distillation for compressing BERT models, specifically TinyBERT, by experimenting with loss functions, mapping methods, and weight tuning, and evaluated these techniques on GLUE benchmark tasks to enhance model efficiency and accuracy.
The use of large transformer-based models such as BERT, GPT, and T5 has led to significant advancements in natural language processing. However, these models are computationally expensive, necessitating model compression techniques that reduce their size and complexity while maintaining accuracy. This project investigates and applies knowledge distillation for BERT model compression, specifically focusing on the TinyBERT student model. We explore various techniques to improve knowledge distillation, including experimentation with loss functions, transformer layer mapping methods, and tuning the weights of attention and representation loss and evaluate our proposed techniques on a selection of downstream tasks from the GLUE benchmark. The goal of this work is to improve the efficiency and effectiveness of knowledge distillation, enabling the development of more efficient and accurate models for a range of natural language processing tasks.