- models are trained using sources of information that are easier to provide than hand-labeled data, where this information is incomplete, inexact, or otherwise less accurate.
- Instead of a subject-matter expert (SME) hand-labeling high-quality data, we can use other techniques that combine diverse sources of data, creating an approximation of labels
- Weak supervision enables these noisy, weakly sourced labels to be combined programmatically to form the training data that can be used to train a model.
Distant Supervision