Can a student Large Language Model perform as well as it's teacher?

TL;DR

This paper reviews knowledge distillation, a technique for transferring knowledge from large teacher models to smaller student models, highlighting its principles, determinants, challenges, and potential in resource-efficient deep learning deployment.

Contribution

It provides a comprehensive overview of knowledge distillation, detailing its foundational principles, critical factors for success, and the challenges faced in practical applications.

Findings

Knowledge distillation effectively transfers performance from teacher to student.

Successful distillation depends on architecture, teacher quality, and hyperparameters.

It offers a promising approach to deploying efficient yet accurate models.

Abstract

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.