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---
license: cc-by-3.0
tags:
- agent
- workflow
- multimodal
- spreadsheet
- pdf
- image
- code
- finance
- accouning
modalities:
- text
- spreadsheet
- pdf
- image
- code
configs:
- config_name: Finch_Dataset_All
data_files:
- split: test
path:
- finch_workflows_test.jsonl
---
# Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows
This repository contains the dataset for **Finch**, an enterprise-level benchmark for evaluating an agent’s ability to act like a skilled finance & accounting expert on real-world workflows.
* **Paper**: _to be added_
* **Project Page**: https://huggingface.co/datasets/FinWorkBench/Finch
* **Code**: https://github.com/FinWorkBench
---
## Dataset Description
Finch focuses on **composite 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** (Enron and various institutions/companies such as World Bank, Canada goverment, etc), including:
- Large and messy **spreadsheets** with multimodal artifacts including text, tables, formulas, charts, pivots, images, etc
- Linked **PDFs and documents** that provide additional business context
We adopt a three-step workflow labeling process:
1. **Inducing workflow types** from real collaborative context in enterprise email threads.
2. **Deriving concrete workflow instances** by analyzing changes across spreadsheet versions.
3. **Conductin meticulous expert annotation** of task instructions, input files, and reference outputs, involving hundreds of hours of expert work.
This process yields **172 enterprise-grade workflows—primarily multi-task composite** — each with carefully written instructions and aligned input/reference files, capturing the intrinsic **compositionality, messiness, multimodality, and collaboration** 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.
Experiment results show that even frontier agents solve fewer than 30% of the workflows, revealing a substantial performance gap for real-world enterprise scenarios.
---
## 📁 Dataset Structure
The instruction-tuning 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)>"
}
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