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.

翻译：表格问答（TableQA）是自然语言处理领域的一项重要任务，要求理解表格并运用多种推理方式来回答问题。本文介绍了TableQAKit，这是首个专为表格问答设计的综合工具包。该工具包构建了一个统一平台，包含丰富的TableQA数据集，并集成了该任务的常用方法以及大型语言模型（LLMs）。用户可根据友好界面添加自己的数据集和方法。此外，令人惊喜的是，利用本工具包中的模块在部分数据集上取得了新的最优结果（SOTA）。最后，TableQAKit还提供了一个基于LLM的TableQA基准测试，用于评估LLMs在表格问答中的作用。TableQAKit以开源形式提供，包含交互式界面（支持可视化操作）和全面的数据资源，便于用户使用。