mlsanityData to 14 Oct 2026

GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction

arXiv 2503.15564 ↗First posted 19 Mar 2025cs.LGcited by 3

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Abstract (arXiv)

Tabular data synthesis involves not only multi-table synthesis but also generating multi-modal data (e.g., strings and categories), which enables diverse knowledge synthesis. However, separating numerical and categorical data has limited the effectiveness of tabular data generation. The GReaT (Generate Realistic Tabular Data) framework uses Large Language Models (LLMs) to encode entire rows, eliminating the need to partition data types. Despite this, the framework's performance is constrained by two issues: (1) tabular data entries lack sufficient semantic meaning, limiting LLM's ability to leverage pre-trained knowledge for in-context learning, and (2) complex multi-table datasets struggle to establish effective relationships for collaboration. To address these, we propose GReaTER (Generate Realistic Tabular Data after data Enhancement and Reduction), which includes: (1) a data semantic enhancement system that improves LLM's understanding of tabular data through mapping, enabling better in-context learning, and (2) a cross-table connecting method to establish efficient relationships across complex tables. Experimental results show that GReaTER outperforms the GReaT framework.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 44 of its 98 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

44 of 98 references.

References · 44

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32 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 6
  2. 2025A Note on Statistically Accurate Tabular Data Generation Using Large Language Modelscited by 0
  3. 2026FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answeringcited by 0