README.md

AI Spreadsheet Benchmark Dataset

The AI Spreadsheet Benchmark captures 53 realistic spreadsheet prompts spanning analysis, enrichment, visualization, and workbook-management workflows. It is designed to evaluate how spreadsheet copilots behave in situ: Do they write formulas? Do charts stay linked to data? Can the output recompute when numbers change?

• Paper: "The AI Spreadsheet Benchmark: Measuring Dynamic Output in Spreadsheet Assistants"
• Dataset: rowshq/aispreadsheetbenchmark
• Metrics: Pass@1, Pass@3, Dynamic Output Rate, Latency

Accessing the dataset

from datasets import load_dataset

benchmark = load_dataset("rowshq/aispreadsheetbenchmark")
questions = benchmark["questions"]        # prompt text, categories, scoring metadata

Task categories

Category	Tasks	Examples
Classic Data Analysis	37	YoY growth columns, lookups, joins, dashboards, cohort tables
Advanced Analysis	5	K-means clustering, forecasting, anomaly detection, custom visualizations
Creating Models	2	Interactive head-to-head calculators, investment simulators
Manage Spreadsheet Elements	3	Conditional formatting, sorting, chart styling, sheet setup
Arithmetic Operations	6	High-precision arithmetic sanity checks

Each prompt record contains:

• Natural-language instructions
• Category and sub-category labels

Recommended evaluation protocol

1. Reset the workbook to the canonical dataset before each run.
2. Issue prompts verbatim. If the assistant asks for clarification, respond neutrally while keeping the task scope fixed.
3. Assess success using the published acceptance criteria (execution checks whenever possible).
4. Assess dynamic output by perturbing underlying data and verifying the response updates automatically (no pasted values or screenshots).
5. Measure latency from prompt submission to assistant completion.
6. Compute metrics: Pass@1, Pass@3 (up to three attempts per task), Dynamic Output Rate, mean/median latency.

Baseline results

Initial evaluation across five assistants (Rows AI Analyst, Excel Copilot, Google Sheets + Gemini, Shortcut, Julius):

Assistant	Pass@1	Pass@3	Dynamic (%)	Mean time (s)
Rows AI Analyst	89	92	74	220
Microsoft Excel Copilot	53	64	8	46
Google Sheets + Gemini	57	64	6	11
Shortcut	83	83	13	222
Julius	75	83	0	30

Detailed per-category tables and visualizations appear in the accompanying technical paper.

Citation

@misc{rowshq2025benchmark,
  title  = {The AI Spreadsheet Benchmark: Measuring Intelligence in Spreadsheet Assistants},
  author = {Samagaio, Álvaro Mendes and Cruz, Henrique and Pereira, Humberto Ayres and Schulz, Torben},
  year   = {2025},
  url    = {https://huggingface.co/datasets/rowshq/aispreadsheetbenchmark/blob/main/technical_paper.pdf}
}

Questions & contributions

Open a discussion or issue on the Hugging Face dataset page if you:

• Find discrepancies in acceptance criteria or scoring instructions
• Want to share new assistant baselines or evaluation tooling
• Plan to extend the benchmark with additional domains or datasets

We welcome contributions that improve documentation, acceptance criteria, or reproducibility assets. Reach out via the dataset page to coordinate substantial updates.