Large Language Models for Data Synthesis
Generating synthetic data that faithfully captures the statistical structure
of real-world distributions is a fundamental challenge in data modeling.
Classical approaches often depend on strong parametric assumptions or manual
structural design and struggle in high-dimensional or heterogeneous domains.
Recent progress in Large Language Models (LLMs) reveals their potential as
flexible, high-dimensional priors over real-world distributions. However, when
applied to data synthesis, standard LLM-based sampling is inefficient,
constrained by fixed context limits, and fails to ensure statistical alignment.
Given this, we introduce LLMSynthor, a general framework for data synthesis
that transforms LLMs into structure-aware simulators guided by distributional
feedback. LLMSynthor treats the LLM as a nonparametric copula simulator for
modeling high-order dependencies and introduces LLM Proposal Sampling to
generate grounded proposal distributions that improve sampling efficiency
without requiring rejection. By minimizing discrepancies in the summary
statistics space, the iterative synthesis loop aligns real and synthetic data
while gradually uncovering and refining the latent generative structure. We
evaluate LLMSynthor in both controlled and real-world settings using
heterogeneous datasets in privacy-sensitive domains (e.g., e-commerce,
population, and mobility) that encompass both structured and unstructured
formats. The synthetic data produced by LLMSynthor shows high statistical
fidelity, practical utility, and cross-data adaptability, positioning it as a
valuable tool across economics, social science, urban studies, and beyond.
6832c2c8ba29b909f4013a6d
Yihong Tang
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2025-05-26T08:12:00.941Z
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Yihong Tang
user
HYTYH
6832c2c8ba29b909f4013a6e
Menglin Kong
6832c2c8ba29b909f4013a6f
Lijun Sun
Large Language Models for Data Synthesis
Generating synthetic data that faithfully captures the statistical structure
of real-world distributions is a fundamental challenge in data modeling.
Classical approaches often depend on strong parametric assumptions or manual
structural design and struggle in high-dimensional or heterogeneous domains.
Recent progress in Large Language Models (LLMs) reveals their potential as
flexible, high-dimensional priors over real-world distributions. However, when
applied to data synthesis, standard LLM-based sampling is inefficient,
constrained by fixed context limits, and fails to ensure statistical alignment.
Given this, we introduce LLMSynthor, a general framework for data synthesis
that transforms LLMs into structure-aware simulators guided by distributional
feedback. LLMSynthor treats the LLM as a nonparametric copula simulator for
modeling high-order dependencies and introduces LLM Proposal Sampling to
generate grounded proposal distributions that improve sampling efficiency
without requiring rejection. By minimizing discrepancies in the summary
statistics space, the iterative synthesis loop aligns real and synthetic data
while gradually uncovering and refining the latent generative structure. We
evaluate LLMSynthor in both controlled and real-world settings using
heterogeneous datasets in privacy-sensitive domains (e.g., e-commerce,
population, and mobility) that encompass both structured and unstructured
formats. The synthetic data produced by LLMSynthor shows high statistical
fidelity, practical utility, and cross-data adaptability, positioning it as a
valuable tool across economics, social science, urban studies, and beyond.
LLMSynthor enhances LLMs for efficient and statistically accurate data synthesis through distributional feedback and proposal sampling.
Large Language Models LLMSynthor nonparametric copula simulator LLM Proposal Sampling summary statistics space synthetic data statistical fidelity practical utility cross-data adaptability