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Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

Xu Guo, Yiqiang Chen

TL;DR

The paper surveys how gigantic, fixed LLMs can generate task-specific synthetic data to address data scarcity and privacy in specialized domains. It organizes methods into prompt engineering, attribute-controlled prompts, verbalizers, and parameter-efficient adaptation, and discusses data-quality metrics, training strategies, and evaluation. It then connects these methods to practical applications—especially in low-resource and medical settings—while candidly outlining challenges such as correctness, hallucination, and privacy concerns, and suggests directions for future research and governance. Overall, the work consolidates a framework for employing synthetic data generation with LLMs to enable efficient training and deployment in challenging domains.

Abstract

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.

Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

TL;DR

The paper surveys how gigantic, fixed LLMs can generate task-specific synthetic data to address data scarcity and privacy in specialized domains. It organizes methods into prompt engineering, attribute-controlled prompts, verbalizers, and parameter-efficient adaptation, and discusses data-quality metrics, training strategies, and evaluation. It then connects these methods to practical applications—especially in low-resource and medical settings—while candidly outlining challenges such as correctness, hallucination, and privacy concerns, and suggests directions for future research and governance. Overall, the work consolidates a framework for employing synthetic data generation with LLMs to enable efficient training and deployment in challenging domains.

Abstract

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.
Paper Structure (14 sections, 1 figure, 1 table)

This paper contains 14 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Generating synthetic training data from LLMs
  3. Prompt engineering
  4. Parameter-efficient task adaptation
  5. Measuring data quality
  6. Training with synthetic data
  7. Applications
  8. Low-resource and long-tail problems
  9. Fast inference and lightweight deployment
  10. Medical Scenarios
  11. Challenges with Synthetic Data and Future Directions
  12. Overcoming Data Limitations
  13. Data privacy and ethical concerns
  14. Conclusion

Figures (1)

  • Figure 1: A general comparison between using LLMs for label-specific synthetic data generation (a) and label words prediction (b). In both cases, the LLMs are frozen and a task-related prompt is provided to condition the LLMs for task adaptation. $\langle X\rangle$ represents the text data and $\langle Y\rangle$ represents the label words.