  Tabular data, as a crucial form of data representation, exists in diverse
formats on the Web. When confronted with complex and irregular tables, manual
modification becomes a laborious task. This paper investigates the performance
of Large Language Models (LLMs) in the context of table editing tasks. Existing
research mainly focuses on regular-shaped tables, wherein instructions are used
to generate code in SQL, Python, or Excel Office-script for manipulating the
tables. Nevertheless, editing tables with irregular structures, particularly
those containing merged cells spanning multiple rows, poses a challenge when
using code. To address this, we introduce the WikiTableEdit dataset. Leveraging
26,531 tables from the WikiSQL dataset, we automatically generate natural
language instructions for six distinct basic operations and the corresponding
outcomes, resulting in over 200,000 instances. Subsequently, we evaluate
several representative large language models on the WikiTableEdit dataset to
demonstrate the challenge of this task. The dataset will be released to the
community to promote related researches.
