# TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

**Authors:** Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, Tao Zhang, Chen Zhou, Kaizhe Shou, Miao Wang, Wufang Zhu, Guoshan Lu, Chao Ye, Yali Ye, Wentao Ye, Yiming Zhang  
**arXiv:** [2307.08674](https://arxiv.org/abs/2307.08674)  
**Date:** 2023-07-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.08674v2/

## Key points

- TableGPT is a unified framework that allows large language models (LLMs) to interact with and operate on tables using natural language input and external functional commands.
- Tables are commonly found in real-world databases and analyzing/manipulating them can be time-consuming for humans.
- TableGPT enables users to seamlessly interact with tables, offering functionalities such as question answering, data manipulation, data visualization, analysis report generation, and automated prediction.
- Global tabular representations allow LLMs to gain a comprehensive understanding of the entire table beyond just meta-information.
- TableGPT is a self-contained system that does not rely on external API interfaces, providing greater flexibility and control over functionality.
- It supports efficient data process flow, query rejection when appropriate, and private deployment for faster domain data fine-tuning while ensuring data privacy.
- TableGPT represents a significant advancement in enabling LLMs to understand and operate on tables using natural language input.

## Summary

The paper introduces TableGPT, a unified framework that enables large language models (LLMs) to interact with and operate on tables using natural language input and external functional commands. Tables are commonly found in real-world databases and analyzing and manipulating them can be time-consuming for humans. The advancements in LLMs have made it possible to bridge the gap between tables and natural language, bringing this capability closer to reality. TableGPT allows users to seamlessly interact with tables, offering 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. The goal is to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the concept of global tabular representations. This novel approach enables LLMs to gain a comprehensive understanding of the entire table beyond just meta-information. 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. One significant advantage of TableGPT is that it is a self-contained system that does not rely on external API interfaces. This offers greater flexibility and control over the system's functionality. Additionally, TableGPT supports efficient data process flow, query rejection when appropriate, and private deployment. These features enable faster domain data fine-tuning while ensuring data privacy, enhancing the framework's adaptability to specific use cases. Overall, TableGPT represents a significant advancement in enabling LLMs to understand and operate on tables using natural language input. Its capabilities extend beyond simple question answering tasks by providing comprehensive functionality for interacting with tabular data.