NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
<|reference_start|>NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries: Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2Formula, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2Formula task by providing a sequence-to-sequence baseline implementation called fCoder. Experimental results validate the effectiveness of fCoder, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCoder with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2Formula task and advocate for further investigation.<|reference_end|>
@article{zhao2024nl2formula:,
    title={NL2Formula: Generating Spreadsheet Formulas from Natural Language
  Queries},
    author={Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu
  Zhang, Yulei Sui, Haidong Zhang},
    journal={arXiv preprint arXiv:2402.14853},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14853},
    primaryClass={cs.CL cs.AI}
}