Chain-of-Table: Evolving Tables in the Reasoning Chain for Table
  Understanding
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.
659e0823b03a0f0e4f5359eb
Zilong Wang
claimed_verified
2024-01-10T08:38:46.368Z
64b722dd2f5a966b97282236
/avatars/aa4ef471069cea0dffc8c7e4ee61a2fd.svg
Zilong Wang
user
zilongwang
659e0823b03a0f0e4f5359ec
Hao Zhang
659e0823b03a0f0e4f5359ed
Chun-Liang Li
659e0823b03a0f0e4f5359ee
Julian Martin Eisenschlos
admin_assigned
2024-01-10T09:21:58.800Z
640f5502a92fedb0e8511d66
https://cdn-avatars.huggingface.co/v1/production/uploads/640f5502a92fedb0e8511d66/3CFOBG_gm4WlQpQXXaQlr.jpeg
Julian Eisenschlos
user
eisenjulian
659e0823b03a0f0e4f5359ef
Vincent Perot
claimed_verified
2024-01-17T08:49:08.221Z
64e3f501bbee0c24b95e20c2
/avatars/bae7b5297e779d91da21ff50389dd90a.svg
Vincent
user
vperot
659e0823b03a0f0e4f5359f0
Zifeng Wang
claimed_verified
2024-01-10T10:39:32.480Z
64da7b3bbb090cef5559fa31
/avatars/4eb059260f06837192c98927254cd89e.svg
Zifeng Wang
user
zifengw
659e0823b03a0f0e4f5359f1
Lesly Miculicich
659e0823b03a0f0e4f5359f2
Yasuhisa Fujii
659e0823b03a0f0e4f5359f3
Jingbo Shang
claimed_verified
2025-02-10T09:52:46.141Z
660655119e3555d648f6c6b5
/avatars/ae1e2c97a08be39b77a9f1a5c2a718ef.svg
Jingbo Shang
user
shangjingbo
659e0823b03a0f0e4f5359f4
Chen-Yu Lee
extracted_confirmed
2024-01-10T05:20:13.007Z
64d1fe784dfd5df7076bfd1a
/avatars/e2bb108d1f6b1383f0e3f263c4747b3d.svg
Chen-Yu Lee
user
chenyulee
659e0823b03a0f0e4f5359f5
Tomas Pfister
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table
  Understanding
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.
The Chain-of-Table framework integrates tabular data into the reasoning chain of large language models (LLMs) to improve performance in table-based question answering and fact verification tasks.
Chain-of-Thought in-context learning tabular reasoning chain intermediate thoughts