A Method for Efficient Structured Data Generation with Large Language Models

With the rapid development of large language model technology, we find ourselves at an interesting juncture regarding the importance of data. The textual data samples from these large unsupervised models are often of poor quality, which in turn produces substandard results. Implicitly, this means that the model struggles to learn the exact underlying structure of the data distribution without supervision, which can manifest as output lacking fidelity and relevance to real data distributions. In order to overcome some of these limitations in data-driven text generation tasks, this paper presents a Efficient Data Generation System (EDGS) for multimodal structured data generation.