TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
<|reference_start|>TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios: We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TableLLM, we have crafted a benchmark tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TableLLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction.Our codes and data are publicly available at https://github.com/TableLLM/TableLLM.<|reference_end|>
@article{zhang2024tablellm:,
    title={TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office
  Usage Scenarios},
    author={Xiaokang Zhang, Jing Zhang, Zeyao Ma, Yang Li, Bohan Zhang, Guanlin
  Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao,
  Juanzi Li, Jie Tang},
    journal={arXiv preprint arXiv:2403.19318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19318},
    primaryClass={cs.CL}
}