Chain-of-Table: Evolving Tables in the Reasoning Chain for Table
  Understanding
Authors: name: Zilong Wang, name: Hao Zhang, name: Chun-Liang Li, name: Julian Martin Eisenschlos, name: Vincent Perot, name: Zifeng Wang, name: Lesly Miculicich, name: Yasuhisa Fujii, name: Jingbo Shang, name: Chen-Yu Lee, name: Tomas Pfister

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.