Disentangling Confidence Score Distribution

Disentangling confidence score distributions aims to improve the reliability of model predictions by better representing a model's uncertainty. Current research focuses on developing methods to calibrate confidence scores, particularly in large language models and other machine learning applications, using techniques like energy-based learning, adaptive decoding, and incorporating listener awareness. This work is crucial for enhancing the trustworthiness and robustness of AI systems across diverse fields, from medical diagnosis to natural language processing, by enabling more informed decision-making based on accurate assessments of model certainty.