Large Language Model for Table Processing: A Survey
<|reference_start|>Large Language Model for Table Processing: A Survey: Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet manipulations, web table question answering, and image table information extraction. Automating these table-centric tasks with Large Language Models (LLMs) or Visual Language Models (VLMs) offers significant public benefits, garnering interest from academia and industry. This survey provides a comprehensive overview of table-related tasks, examining both user scenarios and technical aspects. It covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data analysis. We summarize the training techniques for LLMs and VLMs tailored for table processing. Additionally, we discuss prompt engineering, particularly the use of LLM-powered agents, for various table-related tasks. Finally, we highlight several challenges, including processing implicit user intentions and extracting information from various table sources.<|reference_end|>
@article{lu2024large,
    title={Large Language Model for Table Processing: A Survey},
    author={Weizheng Lu and Jing Zhang and Ju Fan and Zihao Fu and Yueguo Chen and
  Xiaoyong Du},
    journal={arXiv preprint arXiv:2402.05121},
    year={2024},
    doi={10.1007/s11704-024-40763-6},
    number={The article has been accepted by Frontiers of Computer Science
  (FCS), with the DOI: {10.1007/s11704-024-40763-6}},
    archivePrefix={arXiv},
    eprint={2402.05121},
    primaryClass={cs.AI cs.CL}
}