What does the Low Confidence mode do?

Low Confidence mode is designed to identify and present verbatims (input data samples) for which the AI model is uncertain or has low confidence in its predictions. This function is crucial for improving the model's accuracy and effectiveness over time, as it highlights the areas where the model lacks certainty. By submitting these uncertain instances for labeling or review, data scientists and developers can gather new insights, provide corrective labels, and ultimately enhance the quality of the training data. This process helps the model learn from its mistakes and reduces uncertainty in future predictions.

On the other hand, the other options do not align with the purpose of Low Confidence mode. Presenting only high confidence predictions does not serve to improve the model but rather limits the learning process by excluding uncertain cases. Presenting random verbatims for testing distracts from focused learning on specific areas of uncertainty, and showing verbatims from the training set only fails to introduce new data for model refinement.