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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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  > 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/British 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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  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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  # Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows
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+ This repository contains the dataset for **Finch**, an enterprise-grade 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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  > 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 corporations in the EUSES Corpus, investment and securities companies, World Bank, Canadian/British government agencies, and more), 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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  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).
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+ 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.
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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**, involving 1,710 spreadsheets and 2.7 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.
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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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