NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909118082580480 |
|---|---|
| author | Zhao, Wei Hou, Zhitao Wu, Siyuan Gao, Yan Dong, Haoyu Wan, Yao Zhang, Hongyu Sui, Yulei Zhang, Haidong |
| author_facet | Zhao, Wei Hou, Zhitao Wu, Siyuan Gao, Yan Dong, Haoyu Wan, Yao Zhang, Hongyu Sui, Yulei Zhang, Haidong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14853 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries Zhao, Wei Hou, Zhitao Wu, Siyuan Gao, Yan Dong, Haoyu Wan, Yao Zhang, Hongyu Sui, Yulei Zhang, Haidong Computation and Language Artificial Intelligence 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. |
| title | NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.14853 |