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cs.CL2024★ 4 cited
Exploring the Robustness of In-Context Learning with Noisy Labels
Chen Cheng, Xinzhi Yu, Haodong Wen +4
Recently, the mysterious In-Context Learning (ICL) ability exhibited by Transformer architectures, especially in large language models (LLMs), has sparked significant research inte…
cs.CV2024
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning
Xiaoyu Liu, Beitong Zhou, Zuogong Yue +1
Recently, the usage of Contrastive Representation Learning (CRL) as a pre-training technique improves the performance of learning with noisy labels (LNL) methods. However, instead…