| --- |
| 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-grade benchmark for evaluating an agent’s ability to work like a skilled finance & accounting expert (work IQ) on real-world professionel workflows. |
|
|
| * **Paper**: _to be added_ |
| * **Project Page**: https://huggingface.co/datasets/FinWorkBench/Finch |
| * **Code**: https://github.com/FinWorkBench |
|
|
| --- |
|
|
| ## 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: |
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| 1. **Inducing workflow types and instances** from real collaborative context in **enterprise email threads** (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) and designing workflows based on high-quality artifacts from investment and securities companies, World Bank, Canadian/British government agencies, WideSearch, Dabstep, and more. |
| 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**, involving 1,710 spreadsheets and 27 million cells, capturing the intrinsic **compositional, messy, multimodal, 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. |
|
|
| Experiment results show that even frontier agents (GPT 5.1 Pro and Claude Sonnet 4.5 Pro) solve fewer than 40% 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)>" |
| } |
| ``` |
| --- |
|
|
| ## 📣 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! |
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|
