Online Refinement

Online refinement techniques aim to improve the accuracy and reliability of large language models (LLMs) and other deep learning models by iteratively adjusting their outputs based on feedback or additional information. Current research focuses on integrating internal model knowledge with external data sources, employing interactive correction loops, and developing efficient refinement strategies like weight imprinting or normalization layer adjustments. These methods are proving valuable across diverse applications, including question answering, video captioning, text-to-SQL conversion, and robotic scene recognition, enhancing model performance and robustness in complex tasks.