通过增强语义与连接跨表关系，提升表格数据生成真实性。
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
- 用语义映射增强数据含义，让大模型更好理解表格内容。
- 跨表连接方法有效建立复杂表格间的关联关系。
- 相比原框架，生成数据更真实，适合高阶数据合成任务。
表格数据合成不仅涉及多表生成，还需处理多模态数据（如字符串和类别），以实现多样知识融合。然而，将数值与类别数据分离限制了生成效果。GReaT 框架利用大语言模型（LLM）编码整行数据，避免了数据类型拆分。但其性能受限于两点：一是表格条目语义不足，影响 LLM 利用预训练知识进行上下文学习；二是复杂多表数据难以建立有效协作关系。为此，我们提出 GReaTER（Generate Realistic Tabular Data after data Enhancement and Reduction），包含：(1) 数据语义增强系统，通过映射提升表格数据的语义表达，强化上下文学习能力；(2) 跨表连接方法，高效建立复杂表格间的关联。实验表明，GReaTER 显著优于 GReaT 框架。
原文摘要 · Abstract (English)
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.
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