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.

介绍了一项名为 NL2Formula 的新型基准任务，通过提供自然语言查询作为输入，生成与电子表格表格相关的可执行公式，并构建了包含 70,799 个成对的自然语言查询和对应电子表格公式的全面数据集，验证了 fCoder 的有效性和优越性。