A Survey on Model Compression for Large Language Models
It provides a comprehensive overview for researchers and practitioners working on deploying LLMs in resource-limited settings, but it is incremental as a survey paper.
This survey addresses the challenge of large language models' high computational demands by reviewing model compression techniques like quantization, pruning, and knowledge distillation, aiming to enhance efficiency and real-world applicability.
Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use, especially in resource-limited settings. Model compression has emerged as a key research area to address these challenges. This paper presents a survey of model compression techniques for LLMs. We cover methods like quantization, pruning, and knowledge distillation, highlighting recent advancements. We also discuss benchmarking strategies and evaluation metrics crucial for assessing compressed LLMs. This survey offers valuable insights for researchers and practitioners, aiming to enhance efficiency and real-world applicability of LLMs while laying a foundation for future advancements.