The existing Retrieval-Augmented Generation (RAG) systems face significant challenges in terms of cost and effectiveness. On one hand, they need to encode the lengthy retrieved contexts before responding to the input tasks, which imposes substantial computational overhead. On the other hand, directly using generic Large Language Models (LLMs) often leads to sub-optimal answers, while task-specific fine-tuning may compromise the LLMs' general capabilities. To address these challenges, we introduce a novel approach called FlexRAG (Flexible Context Adaptation for RAG). In this approach, the retrieved contexts are compressed into compact embeddings before being encoded by the LLMs. Simultaneously, these compressed embeddings are optimized to enhance downstream RAG performance. A key feature of FlexRAG is its flexibility, which enables effective support for diverse compression ratios and selective preservation of important contexts. Thanks to these technical designs, FlexRAG achieves superior generation quality while significantly reducing running costs. Comprehensive experiments on various question-answering datasets validate our approach as a cost-effective and flexible solution for RAG systems.

本研究解决了现有检索增强生成系统在成本和有效性方面的重大挑战。提出的FlexRAG方法通过将检索到的上下文压缩为紧凑的嵌入，优化了下游RAG性能，从而提升了生成质量并显著降低了运行成本。我们的实验验证了FlexRAG在多种问答数据集上的有效性，证明其为RAG系统提供了一种成本效益高且灵活的解决方案。

更轻更好：面向灵活上下文适应的检索增强生成