Learning with noisy labels

In natural language processing, learning with noisy labels refers to the task of training models when the training dataset contains labels that have been intentionally tampered with. These noisy labels deviate from the original clean distribution, adding complexity and challenges to the learning process. The goal of this task is to design and develop algorithms that can effectively identify and correct noisy labels, thereby improving the robustness and generalization ability of the models. Learning with noisy labels has significant application value, especially in scenarios involving large-scale datasets and learning from positive and unlabeled data, where it can substantially enhance model performance and reliability.