mlsanityData to 7 Oct 2026

AIGT: AI Generative Table Based on Prompt

arXiv 2412.18111 ↗First posted 24 Dec 2024cs.AIcited by 7

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Abstract (arXiv)

Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, generating high-quality synthetic tabular data has become essential. Recent advancements show that large language models (LLMs) can effectively gener-ate realistic tabular data by leveraging semantic information and overcoming the challenges of high-dimensional data that arise from one-hot encoding. However, current methods do not fully utilize the rich information available in tables. To address this, we introduce AI Generative Table (AIGT) based on prompt enhancement, a novel approach that utilizes meta data information, such as table descriptions and schemas, as prompts to generate ultra-high quality synthetic data. To overcome the token limit constraints of LLMs, we propose long-token partitioning algorithms that enable AIGT to model tables of any scale. AIGT achieves state-of-the-art performance on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 24 Dec 2024.

Builds on

23 of 29 references.

References · 23

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11 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
  2. 2026A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Datacited by 5
  3. 2025A Note on Statistically Accurate Tabular Data Generation Using Large Language Modelscited by 0
  4. 2025StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Modelscited by 0
  5. 2026DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Datacited by 0
  6. 2026LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selectioncited by 0
  7. 2026Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agentscited by 0