Data-free Knowledge Distillation

Data-free Knowledge Distillation is a technique applied in the field of natural language processing, aiming to transfer the knowledge from large and complex models to smaller models without using the original training data. This method achieves knowledge transfer by synthesizing data or utilizing the internal structure of the model, thereby enhancing the performance and generalization ability of smaller models while protecting privacy and reducing resource consumption, making it highly valuable for practical applications.