InstructExcel: A Benchmark for Natural Language Instruction in Excel
Justin Payan Swaroop Mishra Mukul Singh Carina Negreanu Christian Poelitz Chitta Baral Subhro Roy Rasika Chakravarthy Benjamin Van Durme Elnaz Nouri
With the evolution of Large Language Models (LLMs) we can solve increasingly
more complex NLP tasks across various domains, including spreadsheets. This
work investigates whether LLMs can generate code (Excel OfficeScripts, a
TypeScript API for executing many tasks in Excel) that solves Excel specific
tasks provided via natural language user instructions. To do so we introduce a
new large-scale benchmark, InstructExcel, created by leveraging the 'Automate'
feature in Excel to automatically generate OfficeScripts from users' actions.
Our benchmark includes over 10k samples covering 170+ Excel operations across
2,000 publicly available Excel spreadsheets. Experiments across various
zero-shot and few-shot settings show that InstructExcel is a hard benchmark for
state of the art models like GPT-4. We observe that (1) using GPT-4 over
GPT-3.5, (2) providing more in-context examples, and (3) dynamic prompting can
help improve performance on this benchmark.