用提示词增强生成高质量合成表格数据，突破大模型长度限制
AIGT: AI Generative Table Based on Prompt
- 利用表描述、模式等元信息做提示词，提升生成质量
- 在20个公开数据集中的14个和两个工业数据集上达到领先性能
- 通过长文本分块算法克服大模型令牌限制，可处理任意规模表格
表格数据占企业数据资产的80%以上，在多个领域至关重要。随着隐私保护和数据共享限制日益严格，生成高质量合成表格数据变得尤为关键。近年来，大型语言模型（LLMs）通过利用语义信息，有效生成真实感强的表格数据，克服了因独热编码带来的高维难题。然而，现有方法未能充分挖掘表格中丰富的信息。为此，我们提出基于提示增强的AI生成表格（AIGT），利用表描述、模式等元信息作为提示，生成超高质量合成数据。为解决LLM的令牌长度限制，我们设计了长文本分块算法，使AIGT可建模任意规模的表格。AIGT在20个公共数据集中的14个以及两个支付宝风控系统的真实工业数据集上表现领先。
原文摘要 · Abstract (English)
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.
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