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

Autor:	Lei, Fangyu, Luo, Tongxu, Yang, Pengqi, Liu, Weihao, Liu, Hanwen, Lei, Jiahe, Huang, Yiming, Wei, Yifan, He, Shizhu, Zhao, Jun, Liu, Kang
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Work in progress
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.15075 Zobrazit plný text záznamu View this record from Arxiv