Low Confidence

Low confidence in machine learning model predictions is a significant research area focusing on improving model reliability and trustworthiness. Current efforts center on developing methods to accurately quantify uncertainty, calibrate confidence scores to reflect true prediction accuracy, and effectively utilize or mitigate low-confidence predictions through techniques like selective prediction, rejection sampling, and confidence-guided decoding. These advancements are crucial for deploying machine learning models in high-stakes applications where reliable predictions are paramount, improving both model performance and user trust.