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.

TableQAKit 是第一个专门设计用于 TableQA 的综合工具包，它提供了丰富的 TableQA 数据集、集成了流行的方法和大型语言模型，并且在一些数据集上实现了新的最佳性能，同时还提供了一个基于大型语言模型的 TableQA 基准测试。

TableQAKit：一套全面实用的基于表格的问题回答工具包

TableQAKit: A Comprehensive and Practical Toolkit for Table-based  Question Answering

In the realm of natural language processing, the understanding of tabular
data has perpetually stood as a focal point of scholarly inquiry. The emergence
of expansive language models, exemplified by the likes of ChatGPT, has ushered
in a wave of endeavors wherein researchers aim to harness these models for
tasks related to table-based question answering. Central to our investigative
pursuits is the elucidation of methodologies that amplify the aptitude of such
large language models in discerning both the structural intricacies and
inherent content of tables, ultimately facilitating their capacity to provide
informed responses to pertinent queries. To this end, we have architected a
distinctive module dedicated to the serialization of tables for seamless
integration with expansive language models. Additionally, we've instituted a
corrective mechanism within the model to rectify potential inaccuracies.
Experimental results indicate that, although our proposed method trails the
SOTA by approximately 11.7% in overall metrics, it surpasses the SOTA by about
1.2% in tests on specific datasets. This research marks the first application
of large language models to table-based question answering tasks, enhancing the
model's comprehension of both table structures and content.

大型语言模型在表格问答任务中的首次应用以增强模型对表格结构和内容的理解。