Prompting LLMs for candidate labels and distilling them into a small model improves annotation accuracy and noise tolerance over single-label annotation.
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Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
Prompting LLMs for candidate labels and distilling them into a small model improves annotation accuracy and noise tolerance over single-label annotation.