arXiv Machine Learning

Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation

The paper introduces SynEval, a six‑dimensional framework for evaluating multi‑table synthetic data. It assesses per‑column fidelity, multivariate structure (including a new conditional distribution check), cross‑table integrity, machine‑learning utility, privacy protection, and edge‑case robustness. SynEval outputs a unified weighted quality score and is generator‑agnostic, working on any real and synthetic CSV folders with automatic schema inference.

arXiv Machine Learning

Oct 2

Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.

By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar

llms agents benchmarks

arXiv AI

Jul 15

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

arXiv:2602. 04029v2 Announce Type: replace-cross Abstract: Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases.

By Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin, Jure Leskovec

safety

arXiv AI

Aug 18

Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents

arXiv:2608. 15109v1 Announce Type: new Abstract: Generating structurally valid synthetic tabular data remains difficult: outputs with high statistical fidelity and downstream utility can still violate semantically meaningful domain constraints.

By Jianxing Zhao, Mao Guan, Dongyu Liu

llms agents

arXiv AI

Jun 11

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

arXiv:2601. 17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities.

By Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le, Fariha Kabir Torsha, Zhimeng Jiang, Minh Khai Bui, Chia-Yuan Chang, Yu-Neng Chuang, Zhen Xiong, Ying Lin, Guanchu Wang, Na Zou

llms

arXiv Machine Learning

Sep 1

Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

arXiv:2608.29674v1 Announce Type: new Abstract: Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative mo...

By Jinmeng Li, Quan Zhang, Hangting Ye, He Zhao, Firas Laakom, Dandan Guo, J\"urgen Schmidhuber

llms safety

arXiv AI

Jul 7

TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data

arXiv:2607. 03926v1 Announce Type: cross Abstract: Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows.

By Jialin Zhang, Fenghao Dong, Yajie Zhou, Vyas Sekar, Shinan Liu

benchmarks safety data-engineering

arXiv AI

Aug 17

Generating Benchmark Health Data Using a Tabular Diffusion Transformer

arXiv:2608. 14496v1 Announce Type: cross Abstract: Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets.

By Hao Yan, Lisa Pilgram, Dan Liu, Linglong Kong, Fida Dankar, Khaled El Emam

llms diffusion benchmarks

arXiv Machine Learning

Jun 9

Disjoint Generation of Synthetic Data

arXiv:2507. 19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models.

By Anton Danholt Lautrup, Muhammad Rajabinasab, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp

safety

arXiv AI

Sep 16

Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors

arXiv:2609.16069v1 Announce Type: cross Abstract: Synthetic tabular data can match real data distributions while still violating the semantic constraints that govern valid tabular rows. This reveals...

By Yili Wang, Ruxue Shi, Mengnan Du, Hangting Ye, Yi Chang, Xin Wang

llms diffusion benchmarks

arXiv Machine Learning

Jun 9

Differentially Private Synthetic Data via APIs 4: Tabular Data

arXiv:2606. 08259v1 Announce Type: new Abstract: This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains.

By Toan Tran, Arturs Backurs, Zinan Lin, Victor Reis, Li Xiong, Sergey Yekhanin

benchmarks safety

arXiv Machine Learning

Jun 10

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real

arXiv:2606. 09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension.

By Manel Slokom, Malek Slokom, Thierno Kante

llms safety

arXiv Machine Learning

Jul 7

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

arXiv:2605. 17758v2 Announce Type: replace Abstract: Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information.

By Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti, Yajat Nagaraj Kiran, Tu Nguyen, Arshia Harish Puthran, Muhjaazee Love, Aadi Sharma, Mahdi Bagheri, Ian Harris, Amir M. Rahmani

llms agents diffusion benchmarks safety

Related stories