AIGT: AI Generative Table Based on Prompt
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917878549184512 |
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| author | Zhang, Mingming Xiao, Zhiqing Lu, Guoshan Wu, Sai Wang, Weiqiang Fu, Xing Yi, Can Zhao, Junbo |
| author_facet | Zhang, Mingming Xiao, Zhiqing Lu, Guoshan Wu, Sai Wang, Weiqiang Fu, Xing Yi, Can Zhao, Junbo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18111 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIGT: AI Generative Table Based on Prompt Zhang, Mingming Xiao, Zhiqing Lu, Guoshan Wu, Sai Wang, Weiqiang Fu, Xing Yi, Can Zhao, Junbo Artificial Intelligence 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. |
| title | AIGT: AI Generative Table Based on Prompt |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.18111 |