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

Chuangtao Ma, Zeyu Zhang, Arijit Khan, Sebastian Schelter, Paul Groth

Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration: 1 upvotes on Hugging Face Daily Papers, #48 of 58 papers on 2026-02-10. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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