用分块批量检索降低大规实体匹配的算力开销
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration
- 通过分块批量检索与生成，减少大模型推理开销
- 相比强基线，运行时间显著下降，匹配效果相当或更优
- 揭示性能与开销的权衡关系，指导高效系统设计
检索增强生成（RAG）能提升大模型在知识密集型任务中的推理能力，但现有RAG流程在大规模实体匹配中面临严重的检索与生成开销。为此，我们提出CE-RAG4EM，一种基于分块的低成本RAG架构，通过分块批量检索与生成降低计算成本。同时构建统一框架，分析并评估面向实体匹配的RAG系统，聚焦分块感知优化与检索粒度。大量实验表明，CE-RAG4EM在保持或提升匹配质量的同时，显著缩短端到端运行时间。进一步分析揭示关键配置参数在性能与开销间存在固有权衡，为设计高效可扩展的实体匹配与数据集成RAG系统提供实用指导。
原文摘要 · Abstract (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.
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