Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era

Dawei Li; Yue Huang; Ming Li; Tianyi Zhou; Xiangliang Zhang; Huan Liu

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arXiv · 2508.19570

Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era

Dawei Li·Yue Huang·Ming Li·Tianyi Zhou·Xiangliang Zhang·Huan Liu

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Abstract

Generative models such as Large Language Models, Diffusion Models, and generative adversarial networks have recently revolutionized the creation of synthetic data, offering scalable solutions to data scarcity, privacy, and annotation challenges in data mining. This tutorial introduces the foundations and latest advances in synthetic data generation, covers key methodologies and practical frameworks, and discusses evaluation strategies and applications. Attendees will gain actionable insights into leveraging generative synthetic data to enhance data mining research and practice. More information can be found on our website: https://syndata4dm.github.io/.

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Dawei Li, Yue Huang, Ming Li, Tianyi Zhou, Xiangliang Zhang, Huan Liu. 2025-08-27. Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era. https://arxiv.org/abs/2508.19570

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