A Method for Efficient Structured Data Generation with Large Language Models

With the rapid advancement of large language model technology, the data utilized for training these models has become increasingly significant. The quality of text data samples produced by large unsupervised models is often inadequate, leading to insufficient outcomes. This inadequacy arises from the model's constrained capacity to precisely emulate the underlying structure of the data without direct supervision, resulting in outputs that may lack the necessary fidelity and relevance to the authentic data distribution. In order to overcome the shortcomings of training data generation for specific language generation tasks, this paper proposes a fast data generation system (Fast Data Generation System, FDGS) that can handle multi-modal and structured data generation. As a method for generating data, FDGS uses clustering abstraction to handle multiple data input types through templates. This approach allows for quick data generation and reduces consumption. FDGS is robust, ensuring stable and reliable performance under various conditions. It is more cost-effective in terms of token usage compared to traditional methods that work on a per-instance basis and do not use templates. By abstracting and clustering different input types, FDGS can efficiently generate data from large models. This system is highly adaptable, making it a great choice for multi-modal data generation tasks. It relies on the basic functions of general large-scale language models and employs a query-answer bidirectional generation mechanism to achieve fast data amplification.