Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
Summary: arXiv:2603.26668v1 Announce Type: cross
Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces two significant challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG, designed to address these challenges effectively.
Introduction
Retrieval-augmented generation (RAG) has emerged as a crucial technique in improving the performance of Large Language Models (LLMs). However, existing frameworks struggle with two critical issues: ensuring retrieval accuracy and maintaining computational efficiency. The introduction of Bridge-RAG aims to tackle these challenges head-on.
Challenges Addressed by Bridge-RAG
- Retrieval Accuracy: Traditional RAG frameworks often fail to connect query entities with relevant document chunks, leading to suboptimal generation quality.
- Computational Efficiency: Many existing systems experience delays in retrieving relevant information, which can hinder real-time applications.
Key Innovations of Bridge-RAG
Bridge-RAG introduces several innovative approaches to enhance both accuracy and efficiency:
- Abstract Concept: By introducing the concept of an abstract to bridge the gap between query entities and document chunks, Bridge-RAG provides a robust semantic understanding.
- Tree Structure Organization: The abstracts are organized into a tree structure, allowing for a multi-level retrieval strategy that ensures ample contextual information retrieval.
- Improved Cuckoo Filter: To enhance efficiency, an improved Cuckoo Filter is implemented, facilitating rapid membership queries and updates during the retrieval process.
- Block Linked List Structure: This design optimizes spatial and temporal locality, improving the overall retrieval speed.
- Entity Temperature-Based Sorting: This mechanism prioritizes more relevant entities, thus optimizing retrieval times.
Performance Results
Extensive experiments have demonstrated that Bridge-RAG significantly outperforms existing RAG frameworks. The results indicate:
- A remarkable 15.65% increase in accuracy over traditional methods.
- A reduction in retrieval time by factors ranging from 10x to 500x, showcasing its efficiency.
Conclusion
Bridge-RAG represents a significant advancement in the field of retrieval-augmented generation. By addressing the critical challenges of accuracy and efficiency through innovative methods, it sets a new benchmark for the performance of Large Language Models. The promising results from extensive testing suggest that Bridge-RAG could play a vital role in the future of AI-driven text generation.