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cs.DB2026
Beyond Scale and Generation: Understanding Language Model-based Entity Matching
Zeyu Zhang, Xue Li, Iacer Calixto +2
Entity matching identifies records that refer to the same real-world entity. Language models can be adapted to this task through bi-encoder, cross-encoder, and generative matcher a…
cs.DB2026
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration
Chuangtao Ma, Zeyu Zhang, Arijit Khan +2
Retrieval-augmented generation (RAG) enhances LLM reasoning in knowledge-intensive tasks, but existing RAG pipelines incur substantial retrieval and generation overhead when applie…
cs.DB2024
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Stefan Grafberger, Paul Groth, Sebastian Schelter
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to the…