title: Chain-of-Table: Evolving Tables in the Reasoning Chain for Table
    Understanding
summary: Table-based reasoning with large language models (LLMs) is a promising
  direction to tackle many table understanding tasks, such as table-based
  question answering and fact verification. Compared with generic reasoning,
  table-based reasoning requires the extraction of underlying semantics from both
  free-form questions and semi-structured tabular data. Chain-of-Thought and its
  similar approaches incorporate the reasoning chain in the form of textual
  context, but it is still an open question how to effectively leverage tabular
  data in the reasoning chain. We propose the Chain-of-Table framework, where
  tabular data is explicitly used in the reasoning chain as a proxy for
  intermediate thoughts. Specifically, we guide LLMs using in-context learning to
  iteratively generate operations and update the table to represent a tabular
  reasoning chain. LLMs can therefore dynamically plan the next operation based
  on the results of the previous ones. This continuous evolution of the table
  forms a chain, showing the reasoning process for a given tabular problem. The
  chain carries structured information of the intermediate results, enabling more
  accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art
  performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM
  choices.
paper_authors._id: 659e0823b03a0f0e4f5359eb
paper_authors.name: Zilong Wang
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paper_authors.fullname: Zilong Wang
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paper_authors.name: Hao Zhang
paper_authors._id: 659e0823b03a0f0e4f5359ed
paper_authors.name: Chun-Liang Li
paper_authors._id: 659e0823b03a0f0e4f5359ee
paper_authors.name: Julian Martin Eisenschlos
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paper_authors.fullname: Julian Eisenschlos
paper_authors.type: user
paper_authors.user: eisenjulian
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paper_authors.name: Vincent Perot
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paper_authors.fullname: Vincent
paper_authors.type: user
paper_authors.user: vperot
paper_authors._id: 659e0823b03a0f0e4f5359f0
paper_authors.name: Zifeng Wang
paper_authors.status: claimed_verified
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paper_authors._id: 64da7b3bbb090cef5559fa31
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paper_authors.fullname: Zifeng Wang
paper_authors.type: user
paper_authors.user: zifengw
paper_authors._id: 659e0823b03a0f0e4f5359f1
paper_authors.name: Lesly Miculicich
paper_authors._id: 659e0823b03a0f0e4f5359f2
paper_authors.name: Yasuhisa Fujii
paper_authors._id: 659e0823b03a0f0e4f5359f3
paper_authors.name: Jingbo Shang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-10T09:52:46.141Z
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paper_authors.avatarUrl: /avatars/ae1e2c97a08be39b77a9f1a5c2a718ef.svg
paper_authors.fullname: Jingbo Shang
paper_authors.type: user
paper_authors.user: shangjingbo
paper_authors._id: 659e0823b03a0f0e4f5359f4
paper_authors.name: Chen-Yu Lee
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-01-10T05:20:13.007Z
paper_authors._id: 64d1fe784dfd5df7076bfd1a
paper_authors.avatarUrl: /avatars/e2bb108d1f6b1383f0e3f263c4747b3d.svg
paper_authors.fullname: Chen-Yu Lee
paper_authors.type: user
paper_authors.user: chenyulee
paper_authors._id: 659e0823b03a0f0e4f5359f5
paper_authors.name: Tomas Pfister
paper_title: Chain-of-Table: Evolving Tables in the Reasoning Chain for Table
    Understanding
paper_summary: Table-based reasoning with large language models (LLMs) is a promising
  direction to tackle many table understanding tasks, such as table-based
  question answering and fact verification. Compared with generic reasoning,
  table-based reasoning requires the extraction of underlying semantics from both
  free-form questions and semi-structured tabular data. Chain-of-Thought and its
  similar approaches incorporate the reasoning chain in the form of textual
  context, but it is still an open question how to effectively leverage tabular
  data in the reasoning chain. We propose the Chain-of-Table framework, where
  tabular data is explicitly used in the reasoning chain as a proxy for
  intermediate thoughts. Specifically, we guide LLMs using in-context learning to
  iteratively generate operations and update the table to represent a tabular
  reasoning chain. LLMs can therefore dynamically plan the next operation based
  on the results of the previous ones. This continuous evolution of the table
  forms a chain, showing the reasoning process for a given tabular problem. The
  chain carries structured information of the intermediate results, enabling more
  accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art
  performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM
  choices.
paper_ai_keywords: Chain-of-Thought Chain-of-Table in-context learning tabular reasoning chain intermediate results state-of-the-art performance WikiTQ FeTaQA TabFact