TableGPT: 將表格、自然語言和指令統一為一體的方法

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

July 17, 2023

作者: 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, Xinglong Deng, Jie Xu, Haobo Wang, Gang Chen, Junbo Zhao

cs.AI

摘要

在現實世界的資料庫中，表格是普遍存在的，需要人類花費大量時間和精力來進行分析和操作。大型語言模型（LLMs）的進步使得使用自然語言輸入與表格進行交互成為可能，將這種能力更接近現實。本文介紹了TableGPT，這是一個統一的精細調校框架，使LLMs能夠理解並操作表格，並使用外部功能命令。它引入了與表格無縫交互的能力，實現了廣泛的功能，如問答、數據操作（例如插入、刪除、查詢和修改操作）、數據可視化、分析報告生成和自動預測。TableGPT旨在為用戶提供便利和可訪問性，使他們能夠輕鬆利用表格數據。TableGPT的核心是全局表格表示的新概念，它使LLMs能夠全面理解整個表格，超越元信息。通過同時訓練LLMs在表格和文本模態上，TableGPT實現了對表格數據的深入理解，以及通過命令鏈執行複雜操作的能力。重要的是，TableGPT具有自包含系統的優勢，而不是依賴外部API接口。此外，它支持高效的數據處理流程、查詢拒絕（在適當時）、私密部署，實現更快的領域數據精細調校，確保數據隱私，從而增強框架對特定用例的適應性。

English

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.