👉 Transformer-based Distillation (TD) is a technique used to enhance the performance of large language models by transferring knowledge from a complex, high-capacity teacher model to a smaller, more efficient student model. In TD, the teacher model generates soft targets (probabilistic outputs) that capture nuanced information and contextual understanding, which are then used to train the student model. Unlike traditional distillation methods that rely on hard targets (categorical labels), TD leverages the richer representations learned by the teacher, allowing the student to learn more sophisticated patterns and improve its performance, especially in tasks requiring fine-grained understanding or low-resource settings. This approach is particularly valuable for deploying powerful language models in resource-constrained environments while maintaining or even surpassing their accuracy.
