SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Year: 2023
Type: conference-paper
Abstract: Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests become a reachable goal. In this work, we propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We propose a set of atomic actions as an abstraction of spreadsheet software functionalities. We further design a state machine-based task planning framework for LLMs to robustly interact with spreadsheets. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline for rigorously benchmarking the ability of LLMs in software control tasks. Our SheetCopilot correctly completes 44.3\% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin. Our project page:https://sheetcopilot.github.io/.
DOI: 10.52202/075280-0220
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G3624945331, 类脑神经网络建模、学习与应用, G7317538059
OpenAlex ID: W7133192099