# Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI

arXiv: https://arxiv.org/abs/2407.21523 · HTML: https://mlsanity.com/paper/2407.21523/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 129 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 30 of 57)

- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [STaSy: Score-based Tabular data Synthesis](https://mlsanity.com/paper/2210.04018/) (2022)
- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis](https://mlsanity.com/paper/2304.12654/) (2023)
- [Diffusion models for missing value imputation in tabular data](https://mlsanity.com/paper/2210.17128/) (2022)
- [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees](https://mlsanity.com/paper/2309.09968/) (2023)
- [Multimodal Table Understanding](https://mlsanity.com/paper/2406.08100/) (2024)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval](https://mlsanity.com/paper/1906.00041/) (2019)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)
- [HYTREL: Hypergraph-enhanced Tabular Data Representation Learning](https://mlsanity.com/paper/2307.08623/) (2023)

## Built on by (most cited first, 15 of 15)

- [Exploring Large Language Models for Feature Selection: A Data-centric Perspective](https://mlsanity.com/paper/2408.12025/) (2024)
- [Retrieval-augmented Large Language Models for Financial Time Series Forecasting](https://mlsanity.com/paper/2502.05878/) (2025)
- [Diffusion and Flow Matching Models for Tabular Data: A Survey](https://mlsanity.com/paper/2502.17119/) (2025)
- [Ensembling Membership Inference Attacks Against Tabular Generative Models](https://mlsanity.com/paper/2509.05350/) (2025)
- [Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis](https://mlsanity.com/paper/2509.18014/) (2025)
- [Reasoning Factual Knowledge in Structured Data with Large Language Models](https://mlsanity.com/paper/2408.12188/) (2024)
- [Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline](https://mlsanity.com/paper/2503.21155/) (2025)
- [Data Augmentation via Diffusion Model to Enhance AI Fairness](https://mlsanity.com/paper/2410.15470/) (2024)
- [Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization](https://mlsanity.com/paper/2504.17355/) (2025)
- [How Well Does Your Tabular Generator Learn the Structure of Tabular Data?](https://mlsanity.com/paper/2503.09453/) (2025)
- [Active Tabular Augmentation via Policy-Guided Diffusion Inpainting](https://mlsanity.com/paper/2605.10315/) (2026)
- [Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation](https://mlsanity.com/paper/2501.07183/) (2025)
- [Interpolation pour l'augmentation de donnees : Application `a la gestion des adventices de la canne a sucre a la Reunion](https://mlsanity.com/paper/2501.12400/) (2025)
- [In-Context Decision Making for Optimizing Complex AutoML Pipelines](https://mlsanity.com/paper/2508.13657/) (2025)
- [Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs](https://mlsanity.com/paper/2512.21915/) (2025)

## Abstract (arXiv)

Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused on tabular data augmentation (TDA) to enhance the original table with additional data, thereby improving downstream ML tasks. Recently, there has been a growing interest in leveraging the capabilities of generative AI for TDA. Therefore, we believe it is time to provide a comprehensive review of the progress and future prospects of TDA, with a particular emphasis on the trending generative AI. Specifically, we present an architectural view of the TDA pipeline, comprising three main procedures: pre-augmentation, augmentation, and post-augmentation. Pre-augmentation encompasses preparation tasks that facilitate subsequent TDA, including error handling, table annotation, table simplification, table representation, table indexing, table navigation, schema matching, and entity matching. Augmentation systematically analyzes current TDA methods, categorized into retrieval-based methods, which retrieve external data, and generation-based methods, which generate synthetic data. We further subdivide these methods based on the granularity of the augmentation process at the row, column, cell, and table levels. Post-augmentation focuses on the datasets, evaluation and optimization aspects of TDA. We also summarize current trends and future directions for TDA, highlighting promising opportunities in the era of generative AI. In addition, the accompanying papers and related resources are continuously updated and maintained in the GitHub repository at https://github.com/SuDIS-ZJU/awesome-tabular-data-augmentation to reflect ongoing advancements in the field.