entity-matching

This study evaluates LLMs as data quality annotators on e-commerce tasks, finding they outperform baselines when background knowledge is required but offer limited advantages for tasks with strong lexical signals, while demonstrating high consistency across runs.

This paper investigates using LLMs as teacher models to label training data for entity matching, showing that student models trained on machine-labeled data perform on par with those trained on manually labeled benchmarks, with significant cost and speed advantages.