基於分塊策略的低成本RAG實體匹配方法：大型語言模型的探索性研究

摘要

檢索增強生成（RAG）技術雖能提升大型語言模型在知識密集型任務中的推理能力，但現有RAG流程應用於大規模實體匹配時會產生顯著的檢索與生成開銷。為解決此侷限性，本文提出CE-RAG4EM——一種基於分塊批量檢索與生成機制的高性價比RAG架構，可有效降低計算成本。我們同時建立了一套針對實體匹配的RAG系統分析評估框架，重點關注分塊感知優化策略與檢索粒度設計。大量實驗表明，相較於強基準模型，CE-RAG4EM在保證可比甚至更優匹配質量的同時，能顯著縮短端到端運行時間。進一步分析揭示，關鍵配置參數會在性能與開銷之間形成內在權衡，這為設計高效可擴展的實體匹配與數據集成RAG系統提供了實踐指導。

English

Retrieval-augmented generation (RAG) enhances LLM reasoning in knowledge-intensive tasks, but existing RAG pipelines incur substantial retrieval and generation overhead when applied to large-scale entity matching. To address this limitation, we introduce CE-RAG4EM, a cost-efficient RAG architecture that reduces computation through blocking-based batch retrieval and generation. We also present a unified framework for analyzing and evaluating RAG systems for entity matching, focusing on blocking-aware optimizations and retrieval granularity. Extensive experiments suggest that CE-RAG4EM can achieve comparable or improved matching quality while substantially reducing end-to-end runtime relative to strong baselines. Our analysis further reveals that key configuration parameters introduce an inherent trade-off between performance and overhead, offering practical guidance for designing efficient and scalable RAG systems for entity matching and data integration.

基於分塊策略的低成本RAG實體匹配方法：大型語言模型的探索性研究

Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration

摘要

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