mlsanityData to 7 Oct 2026

MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data

arXiv 2406.10521 ↗First posted 15 Jun 2024cs.LG cs.AIcited by 7

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can address this, but existing models typically require substantial amounts of data to train effectively, contradicting our objective to solve data scarcity. To address this challenge, we propose a novel framework to generate synthetic tabular data, powered by large language models (LLMs) that emulates the architecture of a Generative Adversarial Network (GAN). By incorporating data generation process as contextual information and utilizing LLM as the optimizer, our approach significantly enhance the quality of synthetic data generation in common scenarios with small sample sizes. Our experimental results on public and private datasets demonstrate that our model outperforms several state-of-art models regarding generating higher quality synthetic data for downstream tasks while keeping privacy of the real data.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 30 of its 66 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

30 of 66 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2020Scaling Laws for Neural Language Modelscited by 6,411
  5. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  6. 2023Large Language Models as Optimizerscited by 830
  7. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
  8. 2019Modeling Tabular data using Conditional GANcited by 695
  9. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  10. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  11. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  12. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
18 more
  1. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  2. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 329
  3. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 319
  4. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  5. 2023Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Spacecited by 183
  6. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  7. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  8. 2024How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environmentscited by 66
  9. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  10. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  11. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  12. 2023TabuLa: Harnessing Language Models for Tabular Data Synthesiscited by 42
  13. 2023TabMT: Generating tabular data with masked transformerscited by 36
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  15. 2023Unified Language Representation for Question Answering over Text, Tables, and Imagescited by 22
  16. 2024EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Modelscited by 14
  17. 2023Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generationcited by 9
  18. 2021Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicinecited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 6.

7 papers.

  1. 2025A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMscited by 13
  2. 2025Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crewscited by 12
  3. 2025AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generationcited by 7
  4. 2024A text-to-tabular approach to generate synthetic patient data using LLMscited by 4
  5. 2025Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generationcited by 3
  6. 2025TAGAL: Tabular Data Generation using Agentic LLM Methodscited by 3
  7. 2025Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
  2. 2025A Comprehensive Survey of Synthetic Tabular Data Generation
  3. 2020Tabular GANs for uneven distribution
  4. 2025FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs
  5. 2025Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
  6. 2023Generating tabular datasets under differential privacy
  7. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
  8. 2025SynLLM: A Comparative Analysis of Large Language Models for Medical Tabular Synthetic Data Generation via Prompt Engineering