SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models

Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, Zhaoxiang Zhang

Keywords: Large Language Model; Task Planning; Embodied AI; Robotics; Software Automation; Decision making

TL;DR: We propose an LLM-based autonomous agent that manipulates complex software by following natural language instructions.

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/.

Submission Number: 292