Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

TL;DR

This paper introduces Adaptive Contextual Compression and a hybrid CAG-RAG framework to improve the scalability and efficiency of knowledge integration in large language models, addressing limitations of existing cache-augmented methods.

Contribution

It proposes novel adaptive compression and hybrid frameworks that enhance CAG's scalability and reasoning capabilities in dynamic knowledge environments.

Findings

Enhanced scalability and efficiency demonstrated on diverse datasets

Improved multi-hop reasoning performance

Effective management of large, dynamic knowledge bases

Abstract

The rapid progress in large language models (LLMs) has paved the way for novel approaches in knowledge-intensive tasks. Among these, Cache-Augmented Generation (CAG) has emerged as a promising alternative to Retrieval-Augmented Generation (RAG). CAG minimizes retrieval latency and simplifies system design by preloading knowledge into the model's context. However, challenges persist in scaling CAG to accommodate large and dynamic knowledge bases effectively. This paper introduces Adaptive Contextual Compression (ACC), an innovative technique designed to dynamically compress and manage context inputs, enabling efficient utilization of the extended memory capabilities of modern LLMs. To further address the limitations of standalone CAG, we propose a Hybrid CAG-RAG Framework, which integrates selective retrieval to augment preloaded contexts in scenarios requiring additional information. Comprehensive evaluations on diverse datasets highlight the proposed methods' ability to enhance scalability, optimize efficiency, and improve multi-hop reasoning performance, offering practical solutions for real-world knowledge integration challenges.