Source Table

Source table research focuses on identifying and characterizing the origins of various phenomena, ranging from data biases in machine learning models to the sources of information used by large language models. Current research employs diverse methods, including machine learning algorithms for risk assessment, uncertainty quantification techniques in 3D reconstruction and natural language processing, and novel metrics for evaluating generative models based on physical principles. Understanding the source of data, biases, and information is crucial for improving model reliability, fairness, and transparency across numerous scientific fields and practical applications, such as financial modeling, medical diagnosis, and AI safety.