README.md

---
license: cc-by-3.0
tags:
- agent
- workflow
- multimodal
- spreadsheet
- pdf
- image
- code
- finance
- accounting
modalities:
- text
- spreadsheet
- pdf
- image
- code

configs:
  - config_name: Finch_Dataset_All
    data_files:
    - split: test
      path:
        - finch_workflows_test.jsonl
---

<img src="figs/finch_workflow.jpeg" width="1000" />

# Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

This repository contains the dataset for **Finch**, an enterprise-grade benchmark for evaluating an agent’s ability to work like a skilled finance & accounting expert (work IQ) on real-world professional workflows.

* **Paper**: https://arxiv.org/abs/2512.13168

---

## Dataset Description

Finch focuses on **messy and long-horizon finance & accounting workflows** that span:

> data entry/import, structuring/formatting, web search, cross-sheet/file retrieval, calculation, financial modeling, validation, translation, visualization, and reporting.

The workflows are derived from **real-world enterprise workspaces** (primarily Enron, as well as corporations in the EUSES Corpus, investment and securities companies, World Bank, Canadian/British government agencies, and more), including:

- Enterprise **email threads** where collaborators naturally describe, discuss, and track workflows
- Large and messy **spreadsheets** with multimodal artifacts including text, tables, formulas, charts, pivots, images, etc 
- Interlinked **PDFs and documents** that provide additional business context  

We adopt a three-step workflow labeling process:

1. **Inducing workflow types and instances** from real collaborative context in **enterprise email threads** ([Enron Corpus](https://en.wikipedia.org/wiki/Enron_Corpus): 500,000 emails from 150 executives and employees).  
2. **Deriving concrete workflow instances** by analyzing changes across **spreadsheet versions** (15,000 versioned spreadsheets from Enron and [EUSES](https://dl.acm.org/doi/10.1145/1082983.1083242)) and designing workflows based on high-quality artifacts from investment and securities companies, World Bank, Canadian/British government agencies, WideSearch, Dabstep, and more.  
3. **Conducting meticulous expert annotation** of task instructions, input files, and reference outputs, involving hundreds of hours of expert work.

<img src="figs/annotation.png" width="1000" />

This process yields **172 enterprise-grade workflows—primarily multi-task composite workflows**, involving 1,710 spreadsheets and 27 million cells, capturing the intrinsic **messy, long-horizon, knowledge-intensive, and collaborative nature** of real-world finance & accounting work. In this release, we provide full annotations for the first 72 workflows, with the remaining 100 to be released in a subsequent update.

<img src="figs/distribution_chart.png" width="1000" />

We conduct both human and automated evaluations of frontier AI systems including GPT5.1, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. GPT 5.1 Pro spends 16 mins for each workflow on average and gets the highest pass rate of 38%, while Claude Sonnet 4.5 passes just 25.0%, revealing a substantial performance gap for real-world enterprise scenarios.

<img src="figs/exp_results.jpeg" width="1000" />

---

## Examples

Example 1: Review the Inv & WC Value Adj summary tab and add the missing cross‑sheet data references to the other worksheets so the roll‑up pulls the correct figures. Return the updated file with those links in place.

<img src="figs/example_1.jpeg" width="1000" />

Example 2: Add a new worksheet named "Scenario3" to the same workbook, mirroring the structure, row/column layout, monthly detail table, and chart area of "Scenario1". For Scenario3, update the hedging assumptions to a balanced allocation: 10-Yr 25%, 5-Yr 20%, 1-Yr 15%, May-Sep 20%, Q3 15%. Keep the note "Maximum Monthly Average Short Position to Cover (July Peak) = 30,508 MW" unchanged; only the new sheet should be added, and formulas may be used within it.

<img src="figs/example_2.jpeg" width="1000" />

Example 3: Transcribe the content from the image into the Excel file.

<img src="figs/example_3.jpeg" width="1000" />

Example 4: Per the red parameters and the Method 1/Method 2 guidance noted in H8 and H9, complete the formulas in columns T and U (starting from1), Note that the starting point for the formulas in columns T and U is 1, representing the initial signal to hold Index 1. In the formulas for columns T and U, 1 represents the signal to hold Index 1, -1 represents the signal to hold Index 2, and 0 represents the signal to make no change. Then complete column I. The method selection in B11 should drive the model so that all cells and charts refresh consistently when switching between methods.

<img src="figs/example_4.jpeg" width="1000" />

---


## 📁 Dataset Structure

The corpus is released in **JSONL** format.  
Each line corresponds to one **workflow-centric example**:

```json
{
  "id": "<workflow identifier>",
  "instruction_en": "<English task instruction for a finance & accounting workflow>",
  "source_files": ["<input file name>", "..."],
  "source_files_urls": ["<input file download URL>", "..."],
  "reference_outputs": {
    "files": ["<reference output file name>"],
    "text": "<textual reference output>"
  },
  "reference_file_urls": ["<reference output file download URL>"],
  "task_type": "<task category (e.g., reporting, modeling)>",
  "business_type": "<business domain (e.g., budgeting, trading)>",
  "task_constraints": "<task constraints (e.g., perform modifications rather than generation from scratch)>"
}
```

Annotations of Finch should not appear in training corpora.

---

## 📣 Feedback & Issues

If you find any issues with the dataset or have suggestions, please open a discussion in the **Community** tab — we value your feedback!

## 📣 Citation
```bibtex
@article{dong2025finch,
  title={Finch: Benchmarking Finance \& Accounting across Spreadsheet-Centric Enterprise Workflows},
  author={Dong, Haoyu and Zhang, Pengkun and Gao, Yan and Dong, Xuanyu and Cheng, Yilin and Lu, Mingzhe and Yakefu, Adina and Zheng, Shuxin},
  journal={arXiv preprint arXiv:2512.13168},
  year={2025}
}
```

**📧 Contact:** [email protected]