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Experiment 1: The student model is trained solely by minimizing the difference between its final output predictions and the teacher model&#x27;s final output predictions.
Experiment 2: In addition to matching the final predictions, the student model is

The application of knowledge distillation to BERT can be performed at multiple levels of its architecture. Beyond matching the final output predictions of the teacher model, it is also possible to distill knowledge from the intermediate hidden layers. This is achieved by incorporating a training loss that encourages the student model's hidden layer outputs to mimic those of the teacher model.

Multi-level Knowledge Distillation in BERT

When using a large 'teacher' model to train a smaller 'student' model, the only way to transfer knowledge is by training the student to replicate the teacher's final output predictions.

A team is using a large language model (the 'teacher') to train a smaller, more efficient 'student' model. Beyond training the student to match the teacher's final predictions, describe an additional method for transferring knowledge from the teacher's internal structure and explain the primary benefit of this approach.

Enhancing Knowledge Transfer in Model Distillation

A machine learning team is developing a compact language model (the 'student') by training it to learn from a much larger, high-performing model (the 'teacher'). They conduct two experiments with identical student model architectures:

*   **Experiment 1:** The student model is trained solely by minimizing the difference between its final output predictions and the teacher model's final output predictions.
*   **Experiment 2:** In addition to matching the final predictions, the student model is 

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