Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional 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 global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond 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. 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, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.

翻译：表格在现实世界数据库中普遍存在，人类需要耗费大量时间和精力对其进行分析与操作。大型语言模型（LLM）的进步使得通过自然语言输入与表格交互成为可能，正将该能力推向实际应用。本文提出TableGPT——一个统一的微调框架，使LLM能够借助外部函数命令理解并操作表格。该框架实现了与表格的无缝交互，支持问答、数据操作（如插入、删除、查询与修改）、数据可视化、分析报告生成以及自动化预测等多样化功能。TableGPT旨在通过赋能用户轻松利用表格数据，提供便捷性与可及性。其核心在于全局表格表示这一创新概念，使LLM能够超越元信息获取对整个表格的全面理解。通过联合训练表格与文本两种模态，TableGPT实现了对表格数据的深度理解，并可通过链式指令执行复杂表格操作。值得强调的是，TableGPT具备自包含系统的优势，无需依赖外部API接口。此外，它支持高效数据流处理、查询拒绝（适当时）与私有化部署，从而加速领域数据微调并保障数据隐私，提升框架对特定用例的适配性。