DataGen: Unified Synthetic Dataset Generation via Large Language Models

TL;DR

DataGen is a versatile framework leveraging large language models to generate diverse, accurate, and controllable synthetic datasets, improving benchmarking and data augmentation for AI systems.

Contribution

The paper introduces DataGen, a novel LLM-powered framework that enhances synthetic data generation with diversity, accuracy, and user control mechanisms, addressing key challenges in the field.

Findings

DataGen produces high-quality, diverse datasets validated through mathematical and factual assessments.

Application of DataGen improves LLM benchmarking and data augmentation effectiveness.

Modules within DataGen significantly enhance data quality and task-specific customization.

Abstract

Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-generated datasets. Despite this, challenges remain in the areas of generalization, controllability, diversity, and truthfulness within the existing generative frameworks. To address these challenges, this paper presents DataGen, a comprehensive LLM-powered framework designed to produce diverse, accurate, and highly controllable datasets. DataGen is adaptable, supporting all types of text datasets and enhancing the generative process through innovative mechanisms. To augment data diversity, DataGen incorporates an attribute-guided generation module and a group checking feature. For accuracy, it employs a code-based mathematical assessment for label verification alongside a retrieval-augmented generation technique for factual validation. The framework also allows for user-specified constraints, enabling customization of the data generation process to suit particular requirements. Extensive experiments demonstrate the superior quality of data generated by DataGen, and each module within DataGen plays a critical role in this enhancement. Additionally, DataGen is applied in two practical scenarios: benchmarking LLMs and data augmentation. The results indicate that DataGen effectively supports dynamic and evolving benchmarking and that data augmentation improves LLM capabilities in various domains, including agent-oriented abilities and reasoning skills.