Cheap Supervision refers to a training approach where the model is trained with minimal or inexpensive labeling efforts. Instead of relying on a large amount of precisely labeled data, cheap supervision methods often leverage heuristics, weak labels, or other cost-effective annotation strategies. The goal is to reduce the manual labeling cost while still achieving acceptable performance.