HASH-RAG：将深度哈希与检索器相结合，实现高效、精细检索与增强生成
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
AI总结：
Hash-RAG 通过深度哈希直接从知识库学习二进制码以提升检索效率，并设计 PGCC 模块增强大模型上下文感知，在多个问答数据集上实现检索时间降低 90% 且 EM 分数提升 1.4-4.3%。
摘要：
检索增强生成（RAG）在扩展到大规模知识库并同时保持上下文相关性时面临效率挑战。我们提出 Hash-RAG，这是一个将深度哈希技术与系统化优化相结合以解决这些局限性的框架。我们的查询直接从知识库代码中学习二进制哈希码，消除了中间特征提取步骤，并显著降低了存储和计算开销。在这一基于哈希的高效检索框架之上，我们为细粒度分块奠定了基础。因此，我们设计了一个提示引导的分块到上下文（Prompt-Guided Chunk-to-Context，PGCC）模块，该模块通过提示工程利用检索到的哈希索引命题及其原始文档片段来增强大语言模型的上下文感知能力。在 NQ、TriviaQA 和 HotpotQA 数据集上的实验评估表明，与传统方法相比，我们的方法在检索时间上实现了 90% 的减少，同时保持了可观的召回性能。此外，所提出的系统在 EM 分数上比检索/非检索基线高出 1.4-4.3%。
英文摘要：
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. Our queries directly learn binary hash codes from knowledgebase code, eliminating intermediate feature extraction steps, and significantly reducing storage and computational overhead. Building upon this hash-based efficient retrieval framework, we establish the foundation for fine-grained chunking. Consequently, we design a Prompt-Guided Chunk-to-Context (PGCC) module that leverages retrieved hash-indexed propositions and their original document segments through prompt engineering to enhance the LLM's contextual awareness. Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. Additionally, The proposed system outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores.
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。
