Confidence Measure

Confidence measures in machine learning aim to quantify a model's certainty in its predictions, improving reliability and trustworthiness, especially in high-stakes applications. Current research focuses on developing and improving these measures for various model types, including large language models and deep neural networks, often employing techniques like Monte Carlo dropout, entropy-based methods, and ensemble diversity. This work is crucial for enhancing the safety and usability of AI systems across diverse fields, from legal NLP and medical diagnosis to earth observation and educational technology, by providing users with a clearer understanding of prediction reliability.