用自适应压缩提升大模型知识缓存能力，解决大规模知识集成效率问题。
Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration
- 提出自适应上下文压缩技术，动态优化预加载知识的使用效率。
- 在多跳推理任务中性能提升显著，支持更大规模知识库扩展。
- 适合需要高效知识集成的工业级应用，如智能客服、知识库问答。
大型语言模型的快速发展推动了知识密集型任务的新方法。其中，缓存增强生成（CAG）作为检索增强生成（RAG）的有前景替代方案，通过预先将知识载入模型上下文，减少检索延迟并简化系统设计。然而，如何有效扩展CAG以应对大规模动态知识库仍面临挑战。本文提出自适应上下文压缩（ACC），一种动态压缩与管理上下文输入的技术，可高效利用现代LLM的扩展记忆能力。为进一步克服单一CAG的局限性，我们设计了混合CAG-RAG框架，结合选择性检索，在需额外信息时补充预加载上下文。在多种数据集上的全面评估表明，所提方法显著提升可扩展性、运行效率，并改善多跳推理性能，为实际知识集成提供了可行解决方案。
原文摘要 · Abstract (English)
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.
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