Model Confidence

Model confidence, the degree to which a model believes its own predictions are correct, is crucial for reliable deployment of machine learning systems, particularly in high-stakes applications. Current research focuses on improving the calibration of model confidence—ensuring that stated confidence accurately reflects prediction accuracy—across various architectures, including large language models (LLMs) and convolutional neural networks, often employing techniques like label smoothing, self-consistency, and novel confidence estimation methods. This work is vital for enhancing trust in AI systems and enabling more effective human-AI collaboration by providing users with a clearer understanding of a model's reliability and limitations.