most citedOn LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

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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…

cs.LG2025

Towards Robust Incremental Learning under Ambiguous Supervision

Rui Wang, Mingxuan Xia, Chang Yao +4

Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotat…