Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration
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.
6989dffebeecc443208d28de
Chuangtao Ma
claimed_verified
2026-02-11T11:19:17.501Z
66c8b6e42400073af3219888
https://cdn-avatars.huggingface.co/v1/production/uploads/66c8b6e42400073af3219888/2EURQi7n7D3GzkdsXMH8r.jpeg
Chuangtao Ma
dylan-ma
user
dylan-ma
6989dffebeecc443208d28df
Zeyu Zhang
6989dffebeecc443208d28e0
Arijit Khan
6989dffebeecc443208d28e1
Sebastian Schelter
6989dffebeecc443208d28e2
Paul Groth
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration
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.
CE-RAG4EM reduces computational overhead in large-scale entity matching by implementing blocking-based batch retrieval and generation while maintaining competitive matching quality.
retrieval-augmented generation large-scale entity matching blocking-based batch retrieval generation overhead entity matching data integration