What does the training mode for label refinement primarily show users?

The training mode for label refinement specifically provides users with instances of verbatims where labels were misapplied. This focus allows users to understand common mistakes in labeling and facilitates targeted learning to improve the accuracy of future labeling endeavors. By examining these misapplied labels, users can develop a sharper eye for nuances in the data, which helps refine their labeling skills and foster greater precision in future tasks.

The emphasis on misapplied labels is essential, as it enhances the user's ability to identify potential errors in their own work, ultimately driving better outcomes in the model's predictive capabilities. This practice of reviewing misapplied labels aids in reinforcing correct labeling practices and allows for effective adjustments to be made in understanding data patterns.

Other choices focus on different aspects of the labeling process, such as showing only verbatims where labels should have been applied or providing examples of high precision labels. However, the direct insight gained from reviewing misapplied labels is more beneficial for refining skills and understanding the intricacies involved in the labeling process.