Core Concepts
Prompt compression toolkit for Large Language Models enhances efficiency and performance.
Abstract
The PCToolkit is a unified plug-and-play solution for compressing prompts in Large Language Models (LLMs). It features cutting-edge prompt compressors, diverse datasets, and metrics for comprehensive performance evaluation. The toolkit boasts a modular design, allowing for easy integration of new datasets and metrics through portable and user-friendly interfaces. Evaluations of the compressors within PCToolkit across various natural language tasks are conducted, including reconstruction, summarization, mathematical problem-solving, question answering, few-shot learning, synthetic tasks, code completion, boolean expressions, multiple choice questions, and lies recognition. The article outlines the toolkit's key components, functionalities, and evaluation results.
Abstract
Prompt compression condenses input prompts efficiently while preserving essential information.
PCToolkit facilitates quick-start services, user-friendly interfaces, and compatibility with common datasets and metrics.
Introduction
Various solutions address challenges in applying Large Language Models (LLMs) to tasks with lengthy textual inputs.
Prompt compression technology offers a strategic solution to condense intricate textual inputs into succinct prompts, enhancing LLM performance within resource constraints.
Toolkit Design
PCToolkit features state-of-the-art compressors, user-friendly interfaces, and a modular design for easy integration of new components.
The toolkit is organized into Compressor, Dataset, Metric, and Runner modules for streamlined experimentation and evaluation.
Related Works
Existing toolkits focus on prompt design intricacies and prompt engineering for language model performance.
Various toolkits like Promptify, ChainForge, Promptotype, and OpenPrompt support prompt engineering and optimization.
Supported Compressors, Datasets, and Metrics
PCToolkit integrates five state-of-the-art prompt compression methods, diverse datasets, and metrics for evaluating performance.
Compressors include Selective Context, LLMLingua, LongLLMLingua, SCRL, and KiS, with support for various tasks and datasets.
Evaluation
Evaluation results across tasks like reconstruction, summarization, mathematical problems, question answering, and more demonstrate the effectiveness of compression techniques.
Performance metrics like BLEU, ROUGE, BERTScore, Edit distance, and Accuracy are used to assess the compressors across different datasets.
Stats
Prompt compression technology enhances LLM performance within resource constraints.
PCToolkit integrates five distinct compressors: Selective Context, LLMLingua, LongLLMLingua, SCRL, and KiS.
Quotes
"Prompt compression technology presents a strategic solution to tackle challenges in applying Large Language Models to tasks with lengthy textual inputs."
"PCToolkit offers a user-friendly and comprehensive resource for prompt compression and evaluation."