Summary of Aigt: Ai Generative Table Based on Prompt, by Mingming Zhang et al.
AIGT: AI Generative Table Based on Prompt
by Mingming Zhang, Zhiqing Xiao, Guoshan Lu, Sai Wu, Weiqiang Wang, Xing Fu, Can Yi, Junbo Zhao
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to generating high-quality synthetic tabular data, essential for privacy protection and data-sharing restrictions in various fields. The method, AI Generative Table (AIGT), leverages meta data information such as table descriptions and schemas to generate ultra-high quality synthetic data using large language models (LLMs). AIGT overcomes token limit constraints by proposing long-token partitioning algorithms, enabling the modeling of tables of any scale. The approach achieves state-of-the-art performance on 16 out of 20 public datasets and two real industry datasets within the Alipay risk control system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create fake data that looks like real data from tables. This is important because we want to protect people’s privacy online. Right now, there are some ways to make fake data using big language models, but they don’t use all the information in a table. The new method uses extra details about the table, like what it’s supposed to be about and how it’s organized, to create super-realistic fake data. This helps us test our systems without using real people’s information. |
Keywords
» Artificial intelligence » Synthetic data » Token
