3 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework
Chuangtao Ma, Zeyu Zhang, Arijit Khan +2
Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensitive to records with ambiguous or spar…
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model
Zeyu Zhang, Paul Groth, Iacer Calixto +1
Entity matching (EM) is the problem of determining whether two records refer to same real-world entity, which is crucial in data integration, e.g., for product catalogs or address…
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…