# An evaluation framework for synthetic data generation models

arXiv: https://arxiv.org/abs/2404.08866 · HTML: https://mlsanity.com/paper/2404.08866/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 13 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Copula Flows for Synthetic Data Generation](https://mlsanity.com/paper/2101.00598/) (2021)
- [TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data](https://mlsanity.com/paper/2207.05295/) (2022)
- [SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources](https://mlsanity.com/paper/2009.09471/) (2020)
- [Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training](https://mlsanity.com/paper/2004.12724/) (2020)
- [A Quantitative and Qualitative Analysis of the Robustness of (Real-World) Election Winners](https://mlsanity.com/paper/2208.13760/) (2022)

## Built on by (most cited first, 5 of 5)

- [Struct-Bench: A Benchmark for Differentially Private Structured Text Generation](https://mlsanity.com/paper/2509.10696/) (2025)
- [Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?](https://mlsanity.com/paper/2501.13955/) (2025)
- [Automated Privacy-Preserving Techniques via Meta-Learning](https://mlsanity.com/paper/2406.16456/) (2024)
- [Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality](https://mlsanity.com/paper/2511.04615/) (2025)
- [ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.20666/) (2026)

## Abstract (arXiv)

Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for enhancing data augmentation for improving machine learning models performance as well as addressing concerns related to sensitive data privacy. Therefore, the necessity of ensuring quality of generated synthetic data, in terms of accurate representation of real data, consists of primary importance. In this work, we present a new framework for evaluating synthetic data generation models' ability for developing high-quality synthetic data. The proposed approach is able to provide strong statistical and theoretical information about the evaluation framework and the compared models' ranking. Two use case scenarios demonstrate the applicability of the proposed framework for evaluating the ability of synthetic data generation models to generated high quality data. The implementation code can be found in https://github.com/novelcore/synthetic_data_evaluation_framework.