---
license: cc-by-3.0
task_categories:
- text-generation
language:
- en
tags:
- multimodal
- agent
- workflow
- 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 cover figure](figs/finch_workflow.jpeg)

# Finch: Benchmarking Finance & Accounting Workflows around Multimodal Enterprise Spreadsheets

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. **Summarizing workflow types** supported by real collaborative enterprise email threads.  
2. **Deriving concrete workflow instances** from versioned spreadsheets and related files using LLMs.  
3. **Meticulous expert annotation** of instructions 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 **complexity, messiness, and multimodality** 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)>"
}