As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG. To overcome the accuracy challenge, we introduce the concept of abstract to bridge query entities and document chunks, providing robust semantic understanding. We organize the abstracts into a tree structure and design a multi-level retrieval strategy to ensure the inclusion of sufficient contextual information. While this hierarchical organization substantially improves answer quality, traversing the tree to locate the abstracts that contain a query entity inevitably introduces additional retrieval overhead. To restore retrieval efficiency, we further integrate the Cuckoo Filter in CFT-RAG, which provides O(1) entity lookup and naturally fits the entity-to-abstract pathway of our framework. Extensive experiments show that Bridge-RAG achieves consistent accuracy improvements across all metrics and up to $1.9\times$ faster retrieval compared to structured RAG baselines.

翻译：作为提升大语言模型生成质量的重要范式，检索增强生成面临检索准确性与计算效率的双重挑战。本文提出一种名为Bridge-RAG的新型检索增强生成框架。为克服准确性挑战，我们引入摘要概念来桥接查询实体与文档片段，提供稳健的语义理解。我们将摘要组织为树形结构，并设计多层级检索策略以保证包含充分的上下文信息。虽然这种层级组织显著提升了回答质量，但在树结构中遍历定位包含查询实体的摘要会不可避免地引入额外检索开销。为恢复检索效率，我们进一步在CFT-RAG中集成布谷鸟过滤器，该结构支持O(1)复杂度的实体查找，并天然适配本框架的实体-摘要路径。广泛实验表明，与结构化RAG基线相比，Bridge-RAG在所有指标上均实现一致的准确性提升，且检索速度提升最高达1.9倍。