SCOPE: A Generative Approach for LLM Prompt Compression

TL;DR

This paper introduces SCOPE, a novel generative prompt compression method that uses semantic chunking and summarization to effectively shorten prompts while preserving quality, outperforming existing token removal techniques.

Contribution

The paper presents a new chunking-and-summarization approach for prompt compression, addressing limitations of token removal methods and enhancing information preservation and coherence.

Findings

Achieves better compression quality than state-of-the-art methods.

Maintains higher stability under high compression ratios.

Proves effectiveness across multiple domain datasets.

Abstract

Prompt compression methods enhance the efficiency of Large Language Models (LLMs) and minimize the cost by reducing the length of input context. The goal of prompt compression is to shorten the LLM prompt while maintaining a high generation quality. However, existing solutions, mainly based on token removal, face challenges such as information loss and structural incoherence, like missing grammar elements in a sentence, or incomplete word phrases after token removal. Such challenges limit the final generation quality of LLM. To overcome these limitations, we present a novel generative prompt compression method. Unlike the existing token removal methods, our method centers at a chunking-and-summarization mechanism. Specifically, our method splits prompt into semantically coherent chunks and rewrites the chunks to be more concise. The chunks are reconstructed into meaningful prompt finally. We design several optimization techniques for the mechanism, including optimized semantic chunking, outlier chunk handling, dynamic compression ratio, compression prioritization, and keyword maintaining. These techniques effectively improve the identifying and preserving of critical information and coherence among texts, as well as providing finer grind control of the compression ratio. We conduct extensive evaluation on question-answering and summarization tasks, with datasets covering multiple different domain. The evaluation shows our method achieves a significantly better compression quality, and higher stability than the state-of-the-art methods, especially under high compression ratio, which proves the effectiveness and practicality of our method.