Calibration Distance

Calibration distance measures how far a prediction model's confidence estimates deviate from perfectly calibrated predictions, aiming to improve the reliability and trustworthiness of model outputs. Current research focuses on developing algorithms to minimize this distance in sequential prediction settings and understanding the gap between model-reported and human-perceived confidence, particularly in large language models (LLMs). This work is crucial for enhancing the usability and dependability of AI systems, especially in applications where accurate confidence assessment is critical, such as medical diagnosis or financial forecasting. Improving calibration is a key step towards building more trustworthy and reliable AI.