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

  • Dawei Li
  • , Yue Huang
  • , Ming Li
  • , Tianyi Zhou
  • , Xiangliang Zhang
  • , Huan Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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/.

Original languageEnglish (US)
Title of host publicationCIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages6833-6836
Number of pages4
ISBN (Electronic)9798400720406
DOIs
StatePublished - Nov 10 2025
Event34th ACM International Conference on Information and Knowledge Management, CIKM 2025 - Seoul, Korea, Republic of
Duration: Nov 10 2025 → Nov 14 2025

Publication series

NameCIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management

Conference

Conference34th ACM International Conference on Information and Knowledge Management, CIKM 2025
Country/TerritoryKorea, Republic of
CitySeoul
Period11/10/25 → 11/14/25

Keywords

  • data mining
  • data synthesis
  • generative models
  • large language models

ASJC Scopus subject areas

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

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