Go to ICLR 2025 Conference homepageSalutary Labeling with Zero Human Annotation
Active learning strategically selects informative unlabeled data points and queries their ground truth labels for model updates. The prevailing assumption in the active learning paradigm is that the acquisition of ground truth labels optimally enhances model performance. However, this assumption may not always hold or maximize learning capacity. Moreover, ground truth annotations incur significant costs due to the need for intensive human labor. In contrast to traditional active learning, this paper proposes salutary labeling, which automatically assigns the most beneficial labels to the most informative samples without human annotation. Specifically, we utilize the influence function, a tool for estimating sample influence, to select newly added samples and assign their salutary labels by choosing the category that maximizes their positive influence. This process eliminates the need for human annotation. Extensive experiments conducted on nine benchmark datasets demonstrate the superior performance of our salutary labeling approach over traditional active learning strategies. Additionally, we provide several in-depth explorations and practical applications including large language model fine-tuning.