SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation

Fuente: arXiv
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Autori principali: Ma, Zeyao, Zhang, Bohan, Zhang, Jing, Yu, Jifan, Zhang, Xiaokang, Zhang, Xiaohan, Luo, Sijia, Wang, Xi, Tang, Jie
Natura: Preprint
Pubblicazione: 2024
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author Ma, Zeyao
Zhang, Bohan
Zhang, Jing
Yu, Jifan
Zhang, Xiaokang
Zhang, Xiaohan
Luo, Sijia
Wang, Xi
Tang, Jie
author_facet Ma, Zeyao
Zhang, Bohan
Zhang, Jing
Yu, Jifan
Zhang, Xiaokang
Zhang, Xiaohan
Luo, Sijia
Wang, Xi
Tang, Jie
contents We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LLMs) in the actual workflow of spreadsheet users. Unlike existing benchmarks that rely on synthesized queries and simplified spreadsheet files, SpreadsheetBench is built from 912 real questions gathered from online Excel forums, which reflect the intricate needs of users. The associated spreadsheets from the forums contain a variety of tabular data such as multiple tables, non-standard relational tables, and abundant non-textual elements. Furthermore, we propose a more reliable evaluation metric akin to online judge platforms, where multiple spreadsheet files are created as test cases for each instruction, ensuring the evaluation of robust solutions capable of handling spreadsheets with varying values. Our comprehensive evaluation of various LLMs under both single-round and multi-round inference settings reveals a substantial gap between the state-of-the-art (SOTA) models and human performance, highlighting the benchmark's difficulty.
format Preprint
id arxiv_https___arxiv_org_abs_2406_14991
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
Ma, Zeyao
Zhang, Bohan
Zhang, Jing
Yu, Jifan
Zhang, Xiaokang
Zhang, Xiaohan
Luo, Sijia
Wang, Xi
Tang, Jie
Computation and Language
Software Engineering
We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LLMs) in the actual workflow of spreadsheet users. Unlike existing benchmarks that rely on synthesized queries and simplified spreadsheet files, SpreadsheetBench is built from 912 real questions gathered from online Excel forums, which reflect the intricate needs of users. The associated spreadsheets from the forums contain a variety of tabular data such as multiple tables, non-standard relational tables, and abundant non-textual elements. Furthermore, we propose a more reliable evaluation metric akin to online judge platforms, where multiple spreadsheet files are created as test cases for each instruction, ensuring the evaluation of robust solutions capable of handling spreadsheets with varying values. Our comprehensive evaluation of various LLMs under both single-round and multi-round inference settings reveals a substantial gap between the state-of-the-art (SOTA) models and human performance, highlighting the benchmark's difficulty.
title SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
topic Computation and Language
Software Engineering
url https://arxiv.org/abs/2406.14991