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This survey aims to explore synthetic data generation methods for supervised fine-tuning in large language models.

April 23, 2026Open Access

Synthetic Data Generation for Supervised Fine-Tuning: A Comprehensive Survey

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This survey aims to explore synthetic data generation methods for supervised fine-tuning in large language models.
Comprehensive analysis of synthetic data generation techniques.
Introduction of a multi-dimensional taxonomy for synthetic data in supervised fine-tuning.
Examination of generation methods and a governance framework to address systemic risks.
Identified a range of generation methods from template-based to multi-agent pipelines.
Highlighted critical roles of user simulation and validation in maintaining dataset quality.
Outlined systemic risks and offered frameworks for responsible deployment of synthetic data.

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This survey analyzes synthetic data generation techniques in supervised fine-tuning, suggesting vital applications and governance frameworks.

Jaewon Lee (2026) studied this question.

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This survey analyzes synthetic data generation techniques in supervised fine-tuning, suggesting vital applications and governance frameworks.