Title: CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Retrieval-Augmented Generation, Tree-RAG, Cuckoo Filter, Knowledge Retrieval

Abstract: 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 baseline methods while maintaining high levels of generative quality. For instance, our method is more than 800% faster than naive Tree-RAG on DART dataset. Our work is available at https://github.com/TUPYP7180/CFT-RAG-2025.