TableQAKit: A Comprehensive and Practical Toolkit for Table-based
  Question Answering
Fangyu Lei Tongxu Luo Pengqi Yang Weihao Liu Hanwen Liu Jiahe Lei Yiming Huang Yifan Wei Shizhu He Jun Zhao Kang Liu
Table-based question answering (TableQA) is an important task in natural
language processing, which requires comprehending tables and employing various
reasoning ways to answer the questions. This paper introduces TableQAKit, the
first comprehensive toolkit designed specifically for TableQA. The toolkit
designs a unified platform that includes plentiful TableQA datasets and
integrates popular methods of this task as well as large language models
(LLMs). Users can add their datasets and methods according to the friendly
interface. Also, pleasantly surprised using the modules in this toolkit
achieves new SOTA on some datasets. Finally, \tableqakit{} also provides an
LLM-based TableQA Benchmark for evaluating the role of LLMs in TableQA.
TableQAKit is open-source with an interactive interface that includes visual
operations, and comprehensive data for ease of use.