  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.
