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TableGPT

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TableGPT is a specifically designed for table analysis. By unifying tables, natural language, and commands into one model, TableGPT comprehends tabular data, understands user intent through natural language, dissects the desired actions, and executes external commands on the table. It subsequently returns the processed results in both tabular and textual explanations to the user. This novel approach simplifies the way users engage with table data, bringing an intuitive feel to data analysis.

Technical report: [PDF] [Arxiv]

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Abstract

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. With the advancements in large language models (LLMs), the ability to interact with tables through natural language input has become a reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external function commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of tabular representations, which are vectorized representations of tables. This is the first successful attempt to extract vector representations from tables and incorporate them into LLMs. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework’s adaptability to specific use cases.

Key Functionality and Contributions

Case Study

We show some cases in Figure 1 - 7. More examples will be released soon.

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About Us

Project members are from College of Computer Science and Technology, and Institute of Computing Innovation of Zhejiang University.

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Citation

You can cite this technical report like this:

@article{2023tablegpt,
    title={TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT},
    author={Zha, Liangyu and Zhou, Junlin and Li, Liyao and Wang, Rui and Huang, Qingyi and Yang, Saisai and Yuan, Jing and Su, Changbao and Li, Xiang and Su, Aofeng and Zhang, Tao and Zhou, Chen and others},
    journal={arXiv preprint arXiv:2307.08674},
    year={2023}
}