Self Supervised Alignment

Self-supervised alignment aims to steer large language models (LLMs) and other machine learning models towards desired behaviors without relying heavily on human-labeled data. Current research focuses on developing algorithms that leverage mutual information, latent space representations, and concept transplantation to achieve alignment, often employing techniques like Direct Preference Optimization (DPO) or adapting existing self-supervised learning methods. These advancements are significant because they reduce the substantial cost and effort associated with traditional supervised alignment methods, potentially leading to safer and more reliable AI systems across diverse applications, including medical image analysis and robotics.