Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers

arXiv:2402.11700v1 Announce Type: new
Abstract: Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. However, the sheer size of these models poses challenges in terms of storage, training and inference due to the inclusion of billions of parameters through layer stacking. While traditional approaches such as model pruning or distillation offer ways for reducing model size, they often come at the expense of performance retention. In our investigation, we systematically explore the approach of …

abstract arxiv capabilities challenges cs.cl inclusion inference language language models language processing large language large language models layer llms natural natural language natural language processing nlp parameters processing storage tasks terms through training type