How can you use knowledge distillation to improve the performance of a smaller Transformer model?

Knowledge distillation is a technique used to transfer knowledge from a larger, more complex model (the "teacher" model) to a smaller, less complex model (the "student" model). In the context of Transformer models, knowledge distillation can be used to improve the performance of a smaller Transformer model by training it to mimic the behavior of a larger, pre-trained Transformer model. The basic idea is to train the student model to not only predict the correct labels but also to match the probability distribution produced by the teacher model. This allows the student model to learn from the teacher model's "soft" ....