Can a student Large Language Model perform as well as it's teacher?
This is an incremental review paper that synthesizes existing knowledge on knowledge distillation for researchers and practitioners in machine learning.
This paper provides a comprehensive overview of knowledge distillation, a technique for transferring knowledge from a high-capacity teacher model to a streamlined student model to address deployment challenges in resource-constrained environments, emphasizing its foundational principles and critical determinants of success.
The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained environments. Knowledge distillation, a technique aiming to transfer knowledge from a high-capacity "teacher" model to a streamlined "student" model, emerges as a promising solution to this dilemma. This paper provides a comprehensive overview of the knowledge distillation paradigm, emphasizing its foundational principles such as the utility of soft labels and the significance of temperature scaling. Through meticulous examination, we elucidate the critical determinants of successful distillation, including the architecture of the student model, the caliber of the teacher, and the delicate balance of hyperparameters. While acknowledging its profound advantages, we also delve into the complexities and challenges inherent in the process. Our exploration underscores knowledge distillation's potential as a pivotal technique in optimizing the trade-off between model performance and deployment efficiency.