Structured Generation

Structured generation focuses on creating data in predefined formats (e.g., JSON, XML, tables) using machine learning models, primarily large language models (LLMs) and diffusion models. Current research emphasizes improving the accuracy, efficiency, and controllability of these models, addressing challenges like "hallucinations" (generating factually incorrect information) and the computational cost of generating complex structures. This field is significant because structured data is crucial for numerous applications, including database population, software development, and scientific simulations, and advancements in structured generation promise to automate and accelerate these processes.