title: TableGPT: Towards Unifying Tables, Nature Language and Commands into One
    GPT
summary: 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.
paper_authors._id: 64b6197cc1452d75f357685c
paper_authors.name: Liangyu Zha
paper_authors._id: 64b6197cc1452d75f357685d
paper_authors.name: Junlin Zhou
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paper_authors.fullname: Junlin Zhou
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paper_authors.user: jlzhou
paper_authors._id: 64b6197cc1452d75f357685e
paper_authors.name: Liyao Li
paper_authors.status: claimed_verified
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paper_authors.fullname: Liyao Li
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paper_authors.user: liyaooi
paper_authors._id: 64b6197cc1452d75f357685f
paper_authors.name: Rui Wang
paper_authors._id: 64b6197cc1452d75f3576860
paper_authors.name: Qingyi Huang
paper_authors._id: 64b6197cc1452d75f3576861
paper_authors.name: Saisai Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-18T08:01:43.993Z
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paper_authors.avatarUrl: /avatars/08ba4c4cd17c04761cd0fab3f8a081ce.svg
paper_authors.fullname: yangsaisai
paper_authors.type: user
paper_authors.user: yangsaisai
paper_authors._id: 64b6197cc1452d75f3576862
paper_authors.name: Jing Yuan
paper_authors._id: 64b6197cc1452d75f3576863
paper_authors.name: Changbao Su
paper_authors._id: 64b6197cc1452d75f3576864
paper_authors.name: Xiang Li
paper_authors._id: 64b6197cc1452d75f3576865
paper_authors.name: Aofeng Su
paper_authors._id: 64b6197cc1452d75f3576866
paper_authors.name: Tao Zhang
paper_authors._id: 64b6197cc1452d75f3576867
paper_authors.name: Chen Zhou
paper_authors._id: 64b6197cc1452d75f3576868
paper_authors.name: Kaizhe Shou
paper_authors._id: 64b6197cc1452d75f3576869
paper_authors.name: Miao Wang
paper_authors._id: 64b6197cc1452d75f357686a
paper_authors.name: Wufang Zhu
paper_authors._id: 64b6197cc1452d75f357686b
paper_authors.name: Guoshan Lu
paper_authors.status: claimed_verified
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paper_authors._id: 649ee7ac1f44b2c0301df24e
paper_authors.avatarUrl: /avatars/00864ad358cd5e0416c03b9587dfc56e.svg
paper_authors.fullname: Guoshan Lu
paper_authors.type: user
paper_authors.user: grason-lu
paper_authors._id: 64b6197cc1452d75f357686c
paper_authors.name: Chao Ye
paper_authors._id: 64b6197cc1452d75f357686d
paper_authors.name: Yali Ye
paper_authors._id: 64b6197cc1452d75f357686e
paper_authors.name: Wentao Ye
paper_authors._id: 64b6197cc1452d75f357686f
paper_authors.name: Yiming Zhang
paper_authors._id: 64b6197cc1452d75f3576870
paper_authors.name: Xinglong Deng
paper_authors._id: 64b6197cc1452d75f3576871
paper_authors.name: Jie Xu
paper_authors._id: 64b6197cc1452d75f3576872
paper_authors.name: Haobo Wang
paper_authors._id: 64b6197cc1452d75f3576873
paper_authors.name: Gang Chen
paper_authors._id: 64b6197cc1452d75f3576874
paper_authors.name: Junbo Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-18T11:13:00.809Z
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paper_authors.avatarUrl: /avatars/c6142931ddfb499e6e08f5bb3a9d35d1.svg
paper_authors.fullname: Jake Zhao
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paper_title: TableGPT: Towards Unifying Tables, Nature Language and Commands into One
    GPT
paper_summary: 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.
paper_ai_summary: TableGPT, a fine-tuned language model with global tabular representations, enables seamless interaction and complex operations on tables via natural language and functional commands.
paper_ai_keywords: TableGPT fine-tuned framework global tabular representations LLMs large language models table and text modalities chain-of-command instructions data manipulation data visualization analysis report generation automated prediction query rejection private deployment domain data fine-tuning data privacy