Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
arXiv preprint arXiv:2401.03426 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Creates a ground-truth dataset via LLM-assisted matching and benchmarks classical ML models against an Indel baseline for identifying duplicate developer identities in OSS commit data.
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Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
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Identifying unique developers in OSS projects: A family of models
Creates a ground-truth dataset via LLM-assisted matching and benchmarks classical ML models against an Indel baseline for identifying duplicate developer identities in OSS commit data.