Synthetic Feedback

Synthetic feedback, the use of artificially generated data to train and improve machine learning models, is a rapidly growing area of research focused on overcoming limitations of human-provided data, such as cost, scarcity, and subjectivity. Current work centers on leveraging large language models to create synthetic feedback for various tasks, including reinforcement learning, automated grading, and factual alignment in clinical summarization, often employing techniques like reward modeling and response selection as auxiliary tasks. This approach offers significant potential for improving the efficiency and effectiveness of training complex AI systems across diverse applications, particularly in scenarios where human-generated data is expensive or difficult to obtain.