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The remarkable performance gains realized by large pretrained models, e.g.,GPT-3, hinge on the massive amounts of data they are exposed to duringtraining. Analogously, distilling such large models to compact models forefficient deployment also necessitates a large amount of (labeled or unlabeled)training data. In this paper, we propose the teacher-guided training (TGT)framework for training a high-quality compact model that leverages theknowledge acquired by pretrained generative models, while obviating the need togo through a large volume of data. TGT exploits the fact that the teacher hasacquired a good representation of the underlying data domain, which typicallycorresponds to a much lower dimensional manifold than the input space.Furthermore, we can use the teacher to explore input space more efficientlythrough sampling or gradient-based methods; thus, making TGT especiallyattractive for limited data or long-tail settings. We formally capture thisbenefit of proposed data-domain exploration in our generalization bounds. Wefind that TGT can improve accuracy on several image classification benchmarksas well as a range of text classification and retrieval tasks.

Teacher Guided Training: An Efficient Framework for Knowledge Transfer