Create README.md

README.md

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+# 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"](https://huggingface.co/datasets/rowshq/aispreadsheetbenchmark/blob/main/technical_paper.pdf)
+- **Dataset:** `rowshq/aispreadsheetbenchmark`
+- **Metrics:** Pass@1, Pass@3, Dynamic Output Rate, Latency
+
+## Accessing the dataset
+
+```python
+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.