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cs.LG2025
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
Mingxuan Xia, Haobo Wang, Yixuan Li +4
Recently, Large Language Models (LLMs) have demonstrated significant potential for data annotation, markedly reducing the labor costs associated with downstream applications. Howev…
cs.LG2025
The Effects of Data Augmentation on Confidence Estimation for LLMs
Rui Wang, Renyu Zhu, Minmin Lin +4
Confidence estimation is crucial for reflecting the reliability of large language models (LLMs), particularly in the widely used closed-source models. Utilizing data augmentation f…