Large Language Models for Data Synthesis

• URL: http://arxiv.org/abs/2505.14752v1
• Date: Tue, 20 May 2025 13:35:38 GMT
• Title: Large Language Models for Data Synthesis
• Authors: Yihong Tang, Menglin Kong, Lijun Sun,
• Abstract summary: Large Language Models (LLMs) have potential as flexible, high-dimensional priors over real-world distributions.<n>We introduce LLM Synthor, a framework for data synthesis that transforms LLMs into structure-aware simulators guided by distributional feedback.<n>By minimizing discrepancies in the summary statistics space, the iterative synthesis loop aligns real and synthetic data.
• Score: 17.333852085464176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs [3.703188184729035]
  Synthetic data generation is an invaluable solution in scenarios where real-world data collection and usage are limited by cost and scarcity.<n>Existing approaches that directly use large language models to generate each record individually impose prohibitive time and cost burdens.<n>We propose a fast, cost-effective method for realistic tabular data synthesis that leverages LLMs to infer and encode each field's distribution into a reusable sampling script.
  arXiv  Detail & Related papers  (2025-07-21T17:51:46Z)
• Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models [0.5156484100374059]
  This paper introduces Synthline, a Product Line (PL) approach that leverages Large Language Models to generate synthetic Requirements Engineering (RE) data.<n>Our analysis reveals that while synthetic datasets exhibit less diversity than real data, they are good enough to serve as viable training resources.<n>Our evaluation shows that combining synthetic and real data leads to substantial performance improvements.
  arXiv  Detail & Related papers  (2025-05-06T07:57:16Z)
• Scaling Laws of Synthetic Data for Language Models [132.67350443447611]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation [49.898152180805454]
  This study is the first to explicitly address inter-column relationship preservation in synthetic tabular data generation.<n>LLM-TabFlow is a novel approach that captures complex inter-column relationships and compress data, while using Score-based Diffusion to model the distribution of the compressed data in latent space.<n>Our results show that LLM-TabFlow outperforms all baselines, fully preserving inter-column relationships while achieving the best balance between data fidelity, utility, and privacy.
  arXiv  Detail & Related papers  (2025-03-04T00:47:52Z)
• Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification [7.357494019212501]
  We propose efficient weighted-loss approaches to align synthetic data with real-world distribution.<n>We empirically assessed the effectiveness of our method on multiple text classification tasks.
  arXiv  Detail & Related papers  (2024-10-28T20:53:49Z)
• Why LLMs Are Bad at Synthetic Table Generation (and what to do about it) [11.266896863556124]
  Synthetic data generation is integral to ML pipelines, e.g., to augment training data, replace sensitive information, and even to power advanced platforms like DeepSeek.<n>While LLMs fine-tuned for synthetic data generation are gaining traction, synthetic table generation remains under-explored compared to text and image synthesis.<n>This paper shows that LLMs, whether used as-is or after traditional fine-tuning, are inadequate for generating synthetic tables.
  arXiv  Detail & Related papers  (2024-06-20T17:52:29Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance [16.047084318753377]
  Imbalanced classification and spurious correlation are common challenges in data science and machine learning.<n>Recent advances have proposed leveraging the flexibility and generative capabilities of large language models to generate synthetic samples.<n>This article develops novel theoretical foundations to systematically study the roles of synthetic samples in addressing imbalanced classification and spurious correlation.
  arXiv  Detail & Related papers  (2024-06-05T21:24:26Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.