Knowledge Distillation for Large Language Models: Model Compression, Efficiency, and Transfer Learning

This survey paper provides a comprehensive overview of knowledge distillation in large language models, which is a technique that has gained huge attention in recent years, because of its potential to reduce computational demands and the parameter size of LLMs while maintaining their performance. The paper explores the fundamentals of knowledge distillation, including the application to natural language processing tasks, and also discusses advanced techniques such as multi-teacher and meta-learning. This survey also tests the role of supervision complexity in effective knowledge distillation, highlighting its importance in the generalization behavior of distilled students. It dives deep into the concept of unlearning in LLMs, which gives the ability to remove harmful or faulty knowledge without affecting unrelated knowledge. The paper evaluates various unlearning methods, including their limitations and challenges, and provides technical comparisons of knowledge distillation and unlearning techniques. The scope of this survey is broad, covering applications and implications of knowledge distillation and unlearning in NLP, also including open challenges and future directions in research. Key findings include the effectiveness of knowledge distillation as a technique for compressing LLMs while maintaining their accuracy, and also the importance of design space exploration in optimizing distillation processes. This survey contributes to the existing publications by providing an analysis of the SOTA techniques of knowledge distillation and unlearning in LLMs. It highlighted areas of progress and identified avenues for future research. The paper is intended for researchers in NLP, offering a comprehensive understanding of the principles, applications, and challenges of knowledge distillation and unlearning in large language models. Ultimately, this survey aims to facilitate further innovation and advancement in the field, enabling the development of more efficient, effective, and responsible LLMs