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--- |
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license: cc-by-3.0 |
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tags: |
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- agent |
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- workflow |
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- multimodal |
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- spreadsheet |
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- pdf |
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- image |
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- code |
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- finance |
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- accouning |
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modalities: |
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- text |
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- spreadsheet |
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- pdf |
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- image |
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- code |
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configs: |
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- config_name: Finch_Dataset_All |
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data_files: |
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- split: test |
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path: |
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- finch_workflows_test.jsonl |
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--- |
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# Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows |
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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. |
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* **Paper**: _to be added_ |
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* **Project Page**: https://huggingface.co/datasets/FinWorkBench/Finch |
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* **Code**: https://github.com/FinWorkBench |
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--- |
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## Dataset Description |
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Finch focuses on **messy and long-horizon finance & accounting workflows** that span: |
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> data entry/import, structuring/formatting, web search, cross-sheet/file retrieval, calculation, financial modeling, validation, translation, visualization, and reporting. |
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The workflows are derived from **real-world enterprise workspaces** (primarily Enron, as well as World Bank, Canadian/Australian government agencies, and other corporations), including: |
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- Enterprise **email threads** where collaborators naturally describe, discuss, and track workflows |
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- Large and messy **spreadsheets** with multimodal artifacts including text, tables, formulas, charts, pivots, images, etc |
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- Interlinked **PDFs and documents** that provide additional business context |
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We adopt a three-step workflow labeling process: |
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1. **Inducing workflow types** from real collaborative context in enterprise email threads. |
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2. **Deriving concrete workflow instances** by analyzing changes across spreadsheet versions. |
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3. **Conductin meticulous expert annotation** of task instructions, input files, and reference outputs, involving hundreds of hours of expert work. |
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This process yields **172 enterprise-grade workflows—primarily multi-task composite** — each with carefully written instructions and aligned input/reference files, 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. |
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Experiment results show that even frontier agents solve fewer than 30% of the workflows, revealing a substantial performance gap for real-world enterprise scenarios. |
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--- |
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## 📁 Dataset Structure |
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The instruction-tuning corpus is released in **JSONL** format. |
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Each line corresponds to one **workflow-centric example**: |
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```json |
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{ |
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"id": "<workflow identifier>", |
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"instruction_en": "<English task instruction for a finance & accounting workflow>", |
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"source_files": ["<input file name>", "..."], |
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"source_files_urls": ["<input file download URL>", "..."], |
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"reference_outputs": { |
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"files": ["<reference output file name>"], |
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"text": "<textual reference output>" |
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}, |
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"reference_file_urls": ["<reference output file download URL>"], |
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"task_type": "<task category (e.g., reporting, modeling)>", |
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"business_type": "<business domain (e.g., budgeting, trading)>" |
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} |
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``` |
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--- |
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## 📣 Feedback & Issues |
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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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