Tabular Data Generation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

7 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 46 papers with code (73 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Modeling Tabular data using Conditional GAN
  1 Jul 2019 9 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  Tabular data usually contains a mix of discrete and continuous columns.
• Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
  18 Sep 2023 5 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Through empirical evaluation across the benchmark, we demonstrate that our approach outperforms deep-learning generation methods in data generation tasks and remains competitive in data imputation.
• TabuLa: Harnessing Language Models for Tabular Data Synthesis
  19 Oct 2023 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified
  In addition, Tabula introduces a token sequence compression strategy that significantly reduces training time while maintaining data quality, alongside a novel token padding method that improves sequence alignment…
• TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
  21 Jan 2025 2 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications.
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark
  25 Oct 2023 2 repositories listed Syntology ran 5 of 5 samples · 0 unverified
  In an empirical study, we evaluate the performance of five state-of-the-art models for tabular data generation on eleven distinct tabular datasets.
• Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
  24 Nov 2022 2 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.
• dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
  31 May 2025 1 repository listed
  We propose dpmm, an open-source library for synthetic data generation with Differentially Private (DP) guarantees.
• Graph Conditional Flow Matching for Relational Data Generation
  21 May 2025 1 repository listed
  Data synthesis is gaining momentum as a privacy-enhancing technology.
• A Note on Statistically Accurate Tabular Data Generation Using Large Language Models
  5 May 2025 1 repository listed
  Large language models (LLMs) have shown promise in synthetic tabular data generation, yet existing methods struggle to preserve complex feature dependencies, particularly among categorical variables.
• A Comprehensive Survey of Synthetic Tabular Data Generation
  23 Apr 2025 1 repository listed
  Tabular data remains one of the most prevalent and critical data formats across diverse real-world applications.
• Diffusion Transformers for Tabular Data Time Series Generation
  10 Apr 2025 1 repository listed Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)
  Inspired by the recent success of DiTs in image and video generation, we extend this framework to deal with heterogeneous data and variable-length sequences.
• TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models
  7 Apr 2025 1 repository listed Syntology ran 9 of 19 samples · 10 unverified
  Ultimately, TabRep provides a simple yet effective approach for training tabular diffusion models under a continuous data manifold.
• LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation
  4 Mar 2025 1 repository listed
  Synthetic tabular data have widespread applications in industrial domains such as healthcare, finance, and supply chains, owing to their potential to protect privacy and mitigate data scarcity.
• Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
  25 Feb 2025 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  Synthetic tabular data generation has traditionally been a challenging problem due to the high complexity of the underlying distributions that characterise this type of data.
• TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
  23 Feb 2025 1 repository listed
  We demonstrate for the first time that prompting a fixed LLM can yield high-quality synthetic tabular data.
• Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation
  20 Feb 2025 1 repository listed
  We propose a new framework for zero-shot generation of synthetic tabular data.
• Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
  20 Dec 2024 1 repository listed
  %In this paper, we start with the observation that imbalanced data training of generative models trained imbalanced dataset which under-represent the minority class.
• Tabular data generation with tensor contraction layers and transformers
  6 Dec 2024 1 repository listed
  In this work, we empirically investigate the potential of using embedding representations on tabular data generation, utilizing tensor contraction layers and transformers to model the underlying distribution of tabular…
• DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
  3 Dec 2024 1 repository listed
  To overcome this, we propose DP-2Stage, a two-stage fine-tuning framework for differentially private tabular data generation.
• TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation
  27 Oct 2024 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  In this paper, we introduce TabDiff, a joint diffusion framework that models all multi-modal distributions of tabular data in one model.
• Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching
  20 Oct 2024 1 repository listed
  It is also highly sensitive to small changes in the initial conditions of the ordinary differential equation (ODE).
• Preserving logical and functional dependencies in synthetic tabular data
  26 Sep 2024 1 repository listed
  In addition to the existing notion of functional dependencies, we introduce the notion of logical dependencies among the attributes in this article.
• Tabular Data Generation using Binary Diffusion
  20 Sep 2024 1 repository listed Syntology ran 10 of 10 samples · 0 unverified
  In this paper, we introduce a novel, lossless binary transformation method that converts any tabular data into fixed-size binary representations, and a corresponding new generative model called Binary Diffusion,…
• Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
  8 Sep 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Due to their data-driven nature, Machine Learning (ML) models are susceptible to bias inherited from data, especially in classification problems where class and group imbalances are prevalent.
• Scaling Up Diffusion and Flow-based XGBoost Models
  28 Aug 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Novel machine learning methods for tabular data generation are often developed on small datasets which do not match the scale required for scientific applications.
• Unmasking Trees for Tabular Data
  8 Jul 2024 1 repository listed
  Despite much work on advanced deep learning and generative modeling techniques for tabular data generation and imputation, traditional methods have continued to win on imputation benchmarks.
• Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
  3 Jul 2024 1 repository listed
  While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on substantial training data, often unavailable…
• Are LLMs Naturally Good at Synthetic Tabular Data Generation?
  20 Jun 2024 1 repository listed
  Large language models (LLMs) have demonstrated their prowess in generating synthetic text and images; however, their potential for generating tabular data -- arguably the most common data type in business and scientific…
• TabPFGen -- Tabular Data Generation with TabPFN
  7 Jun 2024 1 repository listed
  Advances in deep generative modelling have not translated well to tabular data.
• EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models
  15 Apr 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  In this work, we explore the effectiveness of LLMs for generating realistic synthetic tabular data, identifying key prompt design elements to optimize performance.

Syntology lines on 14 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.