CFT-RAG：基于实体树的检索增强生成算法与 cuckoo 过滤器
CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
AI总结：
CFT-RAG通过改进的 cuckoo 过滤器优化树状RAG的实体定位，显著提升检索效率和生成质量。
摘要：
尽管检索增强生成（RAG）通过检索外部知识库并整合生成内容显著提高了生成质量，但在涉及层次结构的树状RAG任务中面临计算效率瓶颈。本文提出了一种基于改进 cuckoo 过滤器的树状RAG加速方法，通过优化检索过程中的实体定位来实现显著的性能提升。树状RAG通过引入层次树结构有效组织实体，而 cuckoo 过滤器作为一种高效的存储结构，支持快速成员查询和动态更新。实验结果表明，我们的方法比朴素的树状RAG快得多，同时保持了高质量的生成能力。当树的数量较大时，我们的方法比朴素的树状RAG快数百倍。我们的工作可在 https://github.com/TUPYP7180/CFT-RAG-2025 上获取。
英文摘要：
Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computational efficiency bottlenecks, particularly in knowledge retrieval tasks involving hierarchical structures for Tree-RAG. This paper proposes a Tree-RAG acceleration method based on the improved Cuckoo Filter, which optimizes entity localization during the retrieval process to achieve significant performance improvements. Tree-RAG effectively organizes entities through the introduction of a hierarchical tree structure, while the Cuckoo Filter serves as an efficient data structure that supports rapid membership queries and dynamic updates. The experiment results demonstrate that our method is much faster than naive Tree-RAG while maintaining high levels of generative quality. When the number of trees is large, our method is hundreds of times faster than naive Tree-RAG. Our work is available at https://github.com/TUPYP7180/CFT-RAG-2025.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
