Explicit Supervision

Explicit supervision in machine learning focuses on improving model training by providing more informative and accurate guidance, moving beyond simple next-token prediction or readily available labeled data. Current research explores diverse approaches, including using debate frameworks to enhance evaluator accuracy, developing self-supervised methods that leverage generative models and forward models to create training data, and refining techniques like knowledge distillation to optimize the transfer of knowledge from teacher to student models. These advancements aim to reduce the reliance on extensive human annotation, thereby improving the efficiency and scalability of training complex AI models for various applications, including natural language processing, computer vision, and speech recognition.