Self Enhancement

Self-enhancement in machine learning focuses on enabling models to iteratively improve their performance through autonomous learning and refinement, moving beyond passive training paradigms. Current research explores iterative self-alignment methods for large language models (LLMs), leveraging self-generated data for continuous improvement and employing multi-task frameworks to enhance performance in specific domains or tasks. These advancements aim to reduce reliance on extensive human annotation and improve model robustness and accuracy across various applications, including natural language processing and image enhancement.