LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion

• URL: http://arxiv.org/abs/2503.02161v2
• Date: Thu, 07 Aug 2025 08:11:00 GMT
• Title: LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion
• Authors: Yunbo Long, Liming Xu, Alexandra Brintrup,
• Abstract summary: Synthetic datasets must maintain domain-specific logical consistency.<n>Existing generative models often overlook these inter-column relationships.<n>This study presents the first method to effectively preserve inter-column relationships without requiring domain knowledge.
• Score: 49.898152180805454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic tabular data are increasingly being used to replace real data, serving as an effective solution that simultaneously protects privacy and addresses data scarcity. However, in addition to preserving global statistical properties, synthetic datasets must also maintain domain-specific logical consistency**-**especially in complex systems like supply chains, where fields such as shipment dates, locations, and product categories must remain logically consistent for real-world usability. Existing generative models often overlook these inter-column relationships, leading to unreliable synthetic tabular data in real-world applications. To address these challenges, we propose LLM-TabLogic, a novel approach that leverages Large Language Model reasoning to capture and compress the complex logical relationships among tabular columns, while these conditional constraints are passed into a Score-based Diffusion model for data generation in latent space. Through extensive experiments on real-world industrial datasets, we evaluate LLM-TabLogic for column reasoning and data generation, comparing it with five baselines including SMOTE and state-of-the-art generative models. Our results show that LLM-TabLogic demonstrates strong generalization in logical inference, achieving over 90% accuracy on unseen tables. Furthermore, our method outperforms all baselines in data generation by fully preserving inter-column relationships while maintaining the best balance between data fidelity, utility, and privacy. This study presents the first method to effectively preserve inter-column relationships in synthetic tabular data generation without requiring domain knowledge, offering new insights for creating logically consistent real-world tabular data.

Related papers

• Generating Synthetic Relational Tabular Data via Structural Causal Models [0.0]
  We develop a novel framework that generates realistic synthetic relational data including causal relationships across tables.<n>Our experiments confirm that this framework is able to construct relational datasets with complex inter-table dependencies mimicking real-world scenarios.
  arXiv  Detail & Related papers  (2025-07-04T12:27:23Z)
• RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models [83.6013616017646]
  RelDiff is a novel diffusion generative model that synthesizes complete relational databases by explicitly modeling their foreign key graph structure.<n>RelDiff consistently outperforms prior methods in producing realistic and coherent synthetic relational databases.
  arXiv  Detail & Related papers  (2025-05-31T21:01:02Z)
• Large Language Models for Data Synthesis [17.333852085464176]
  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.
  arXiv  Detail & Related papers  (2025-05-20T13:35:38Z)
• GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction [9.784347635082232]
  We propose GReaTER to generate realistic Tabular Data. GReaTER includes a data semantic enhancement system and a cross-table connecting method. Experimental results show that GReaTER outperforms the GReaT framework.
  arXiv  Detail & Related papers  (2025-03-19T04:16:05Z)
• Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation [0.7373617024876725]
  We demonstrate the ability to generate high-language tabular data without task-specific fine-tuning or access to real-world data for pre-training.<n>To benchmark GPT-4o, we compared the fidelity and privacy of LLM-generated synthetic data against data generated with the conditional generative adversarial network (CTGAN)<n>Despite the zero-shot approach, GPT-4o outperformed CTGAN in preserving means, 95% confidence intervals, bivariate correlations, and data privacy of RWD, even at amplified sample sizes.
  arXiv  Detail & Related papers  (2025-02-20T12:56:16Z)
• Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation [49.898152180805454]
  This paper proposes three evaluation metrics designed to assess the preservation of logical relationships.<n>We validate these metrics by assessing the performance of both classical and state-of-the-art generation methods on a real-world industrial dataset.
  arXiv  Detail & Related papers  (2025-02-06T13:13:26Z)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Generating Realistic Tabular Data with Large Language Models [49.03536886067729]
  Large language models (LLM) have been used for diverse tasks, but do not capture the correct correlation between the features and the target variable. We propose a LLM-based method with three important improvements to correctly capture the ground-truth feature-class correlation in the real data. Our experiments show that our method significantly outperforms 10 SOTA baselines on 20 datasets in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-29T04:14:32Z)
• Synthesizing Text-to-SQL Data from Weak and Strong LLMs [68.69270834311259]
  The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to- tasks. We introduce a synthetic data approach that combines data produced by larger, more powerful models with error information data generated by smaller, not well-aligned models.
  arXiv  Detail & Related papers  (2024-08-06T15:40:32Z)
• EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models [39.347666307218006]
  Large language models (LLMs) have demonstrated remarkable in-context learning capabilities across diverse applications.<n>We introduce EPIC, a novel approach that leverages balanced, grouped data samples and consistent formatting with unique variable mapping to guide LLMs in generating accurate synthetic data across all classes, even for imbalanced datasets.
  arXiv  Detail & Related papers  (2024-04-15T17:49:16Z)
• FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation [5.824064631226058]
  We introduce textitFederated Tabular Diffusion (FedTabDiff) for generating high-fidelity mixed-type tabular data without centralized access to the original datasets. FedTabDiff realizes a decentralized learning scheme that permits multiple entities to collaboratively train a generative model while respecting data privacy and locality. Experimental evaluations on real-world financial and medical datasets attest to the framework's capability to produce synthetic data that maintains high fidelity, utility, privacy, and coverage.
  arXiv  Detail & Related papers  (2024-01-11T21:17:50Z)
• IRG: Generating Synthetic Relational Databases using Deep Learning with Insightful Relational Understanding [13.724085637262654]
  We propose incremental generator (IRG) that successfully handles ubiquitous real-life situations.<n>IRG ensures the preservation of relational schema integrity, offers a deep understanding of relationships beyond direct ancestors and descendants.<n> Experiments on three open-source real-life relational datasets in different fields at different scales demonstrate IRG's advantage in maintaining the synthetic data's relational schema validity and data fidelity and utility.
  arXiv  Detail & Related papers  (2023-12-23T07:47:58Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders [47.89542334125886]
  We combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. The results indicate that real databases' structures are accurately preserved in the resulting synthetic datasets.
  arXiv  Detail & Related papers  (2022-11-30T10:40:44Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

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.