WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks
Abstract
Existing ML benchmarks lack the depth and diversity of annotations needed for evaluating models on business process management (BPM) tasks. BPM is the practice of documenting, measuring, improving, and automating enterprise workflows. However, research has focused almost exclusively on one task - full end-to-end automation using agents based on multimodal foundation models (FMs) like GPT-4. This focus on automation ignores the reality of how most BPM tools are applied today - simply documenting the relevant workflow takes 60% of the time of the typical process optimization project. To address this gap we present WONDERBREAD, the first benchmark for evaluating multimodal FMs on BPM tasks beyond automation. Our contributions are: (1) a dataset containing 2928 documented workflow demonstrations; (2) 6 novel BPM tasks sourced from real-world applications ranging from workflow documentation to knowledge transfer to process improvement; and (3) an automated evaluation harness. Our benchmark shows that while state-of-the-art FMs can automatically generate documentation (e.g. recalling 88% of the steps taken in a video demonstration of a workflow), they struggle to re-apply that knowledge towards finer-grained validation of workflow completion (F1 < 0.3). We hope WONDERBREAD encourages the development of more "human-centered" AI tooling for enterprise applications and furthers the exploration of multimodal FMs for the broader universe of BPM tasks. We publish our dataset and experiments here: https://github.com/HazyResearch/wonderbread
Cite
@article{arxiv.2406.13264,
title = {WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks},
author = {Michael Wornow and Avanika Narayan and Ben Viggiano and Ishan S. Khare and Tathagat Verma and Tibor Thompson and Miguel Angel Fuentes Hernandez and Sudharsan Sundar and Chloe Trujillo and Krrish Chawla and Rongfei Lu and Justin Shen and Divya Nagaraj and Joshua Martinez and Vardhan Agrawal and Althea Hudson and Nigam H. Shah and Christopher Re},
journal= {arXiv preprint arXiv:2406.13264},
year = {2024}
}