Recent literature has shown that large language models (LLMs) are generally
excellent few-shot reasoners to solve text reasoning tasks. However, the
capability of LLMs on table reasoning tasks is yet to be explored. In this
paper, we aim at understanding how well LLMs can perform table-related tasks
with few-shot in-context learning. Specifically, we evaluated LLMs on popular
table QA and fact verification datasets like WikiTableQuestion, FetaQA,
TabFact, and FEVEROUS and found that LLMs are competent at complex reasoning
over table structures, though these models are not pre-trained on any table
corpus. When combined with `chain of thoughts' prompting, LLMs can achieve very
strong performance with only a 1-shot demonstration, even on par with some SoTA
models. We show that LLMs are even more competent at generating comprehensive
long-form answers on FetaQA than tuned T5-large. We further manually studied
the reasoning chains elicited from LLMs and found that these reasoning chains
are highly consistent with the underlying semantic form. We believe that LLMs
can serve as a simple yet generic baseline for future research. The code and
data are released in this https URL.

本文旨在研究大型语言模型在表格相关任务中的能力，发现当结合 “思维链” 提示时，大型语言模型可以在只有 1 个样本的情况下达到与某些 SotA 模型相当的表现。