LLM-labeled training sets for entity matching produce student models with F1 scores within 2 points of benchmark-trained models on five datasets at a cost of $28-41 versus 470 hours of manual work.
Large language models as annotators: Enhancing generalization of nlp models at minimal cost,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Occupational prompting of open-weight LLMs elicits structured value patterns in Inglehart-Welzel cultural space, extending prior nationality-based cultural bias evaluations.
citing papers explorer
-
Labeling Training Data for Entity Matching Using Large Language Models
LLM-labeled training sets for entity matching produce student models with F1 scores within 2 points of benchmark-trained models on five datasets at a cost of $28-41 versus 470 hours of manual work.
-
Occupational Prompting Reveals Cultural Bias in Large Language Models
Occupational prompting of open-weight LLMs elicits structured value patterns in Inglehart-Welzel cultural space, extending prior nationality-based cultural bias evaluations.