PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large
  Language Models
Jinyi Li Yihuai Lan Lei Wang Hao Wang
Prompt compression is an innovative method for efficiently condensing input
prompts while preserving essential information. To facilitate quick-start
services, user-friendly interfaces, and compatibility with common datasets and
metrics, we present the Prompt Compression Toolkit (PCToolkit). This toolkit is
a unified plug-and-play solution for compressing prompts in Large Language
Models (LLMs), featuring cutting-edge prompt compressors, diverse datasets, and
metrics for comprehensive performance evaluation. PCToolkit boasts a modular
design, allowing for easy integration of new datasets and metrics through
portable and user-friendly interfaces. In this paper, we outline the key
components and functionalities of PCToolkit. We conducted evaluations of the
compressors within PCToolkit across various natural language tasks, including
reconstruction, summarization, mathematical problem-solving, question
answering, few-shot learning, synthetic tasks, code completion, boolean
expressions, multiple choice questions, and lies recognition.