TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering
This provides a practical toolkit for researchers and practitioners in natural language processing working on table-based question answering, though it is incremental as it builds on existing methods and datasets.
The authors tackled the lack of a comprehensive toolkit for table-based question answering by introducing TableQAKit, which integrates datasets, methods, and large language models, achieving new state-of-the-art results on some datasets.
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.