  A cost-effective alternative to manual data labeling is weak supervision
(WS), where data samples are automatically annotated using a predefined set of
labeling functions (LFs), rule-based mechanisms that generate artificial labels
for the associated classes. In this work, we investigate noise reduction
techniques for WS based on the principle of k-fold cross-validation. We
introduce a new algorithm ULF for Unsupervised Labeling Function correction,
which denoises WS data by leveraging models trained on all but some LFs to
identify and correct biases specific to the held-out LFs. Specifically, ULF
refines the allocation of LFs to classes by re-estimating this assignment on
highly reliable cross-validated samples. Evaluation on multiple datasets
confirms ULF's effectiveness in enhancing WS learning without the need for
manual labeling.
