Distillation of Large Language Models for Text Simplification

Abstract

This work presents a comprehensive methodology for harnessing the capabilities of Large Language Models to address specific Natural Language Processing tasks, with a focus on Text Simplification. While LLMs have demonstrated their prowess in tackling a wide range of NLP challenges, their demanding computational requirements can render them impractical for real-time online inference. In response to this limitation, we suggest the concept of text distillation, a technique aimed at effectively transferring the knowledge stored within LLMs to more compact and computationally efficient neural networks.