InstructExcel: A Benchmark for Natural Language Instruction in Excel

Justin Payan, Swaroop Mishra, Mukul Singh, Carina Suzana Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, Elnaz Nouri

Submission Type: Regular Long Paper

Submission Track: Resources and Evaluation

Submission Track 2: Theme Track: Large Language Models and the Future of NLP

Keywords: Code generation, program synthesis, benchmark, large language models

TL;DR: We introduce a new benchmark for code generation from natural language instructions in Excel.

Abstract: 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.

Submission Number: 4170