Large Language Models (LLMs) like ChatGPT have transformed numerous fields by leveraging their extensive reasoning and generalization capabilities. However, as the complexity of prompts increases, with techniques like chain-of-thought (CoT) and in-context learning (ICL) becoming more prevalent, the computational demands skyrocket. This paper introduces LLMLingua, a sophisticated prompt compression method designed to mitigate these challenges. By compressing prompts into a more compact form without significant loss of semantic integrity, LLMLingua enables faster inference and reduced computational costs, promising up to 20x compression rates with minimal performance degradation. …