Decomposed Automation Correction

Decomposed automation correction aims to improve the accuracy and efficiency of automated systems, particularly in complex tasks like text-to-SQL conversion and scientific research. Current research focuses on leveraging large language models (LLMs) and other AI techniques, such as neural networks, reinforcement learning, and finite automata, to decompose complex problems into smaller, more manageable sub-tasks for improved correction. This approach holds significant promise for enhancing the reliability and usability of automated systems across diverse fields, from manufacturing and logistics to healthcare and scientific discovery.