Edge 451: Is One Teacher Enough? Understanding Multi-Teacher Distillation
Enhancing the distillation process using more than one teacher.
In this issue:
An introduction to multi-teacher distillation.
An analysis of the MT-BERT multi-teacher distillation method.
A review of the Portkey framework for LLM guardrailing.
💡 ML Concept of the Day: Understanding Multi-Teacher Distillation
Distillation is typically explained using a teacher-student architecture, where we often conceptualize it as involving a single teacher model. However, there are many scenarios where this process can be enhanced by using multiple teachers. For example, a single-teacher model can produce biased students that focus on a limited form of knowledge.
An alternative to the traditional approach is to use multiple teachers in a method known as multi-teacher distillation. A simple example of this approach might involve several teacher models, each specializing in a specific type of knowledge, such as feature-based or response-based information. Their outputs are then averaged to create the final student model. The result is a more robust student model, even though it is more costly to produce.