@inproceedings{duesterwald-etal-2025-flow,
title = "{FLOW}-{BENCH}: Towards Conversational Generation of Enterprise Workflows",
author = "Duesterwald, Evelyn and
Huo, Siyu and
Isahagian, Vatche and
Jayaram, K. R. and
Kumar, Ritesh and
Muthusamy, Vinod and
Oum, Punleuk and
Saha, Debashish and
Thomas, Gegi and
Venkateswaran, Praveen",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.100/",
pages = "1426--1436",
ISBN = "979-8-89176-333-3",
abstract = "Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts.This paper contributes (i) FLOW-BENCH, a high quality dataset of paired NL instructions and business process definitions toevaluate NL-based BPA tools, and support research in this area, and (ii) FLOW-GEN,our approach to utilize LLMs to translate NL into an intermediate Python representation that facilitates final conversion into widely adopted business process definition languages, such as BPMN and DMN. We bootstrap FLOW-BENCH by demonstrating how it can be used to evaluate the components of FLOW-GEN across eight LLMs. We hope that FLOW-GEN and FLOW-BENCHcatalyze further research in BPA."
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<abstract>Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts.This paper contributes (i) FLOW-BENCH, a high quality dataset of paired NL instructions and business process definitions toevaluate NL-based BPA tools, and support research in this area, and (ii) FLOW-GEN,our approach to utilize LLMs to translate NL into an intermediate Python representation that facilitates final conversion into widely adopted business process definition languages, such as BPMN and DMN. We bootstrap FLOW-BENCH by demonstrating how it can be used to evaluate the components of FLOW-GEN across eight LLMs. We hope that FLOW-GEN and FLOW-BENCHcatalyze further research in BPA.</abstract>
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%0 Conference Proceedings
%T FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows
%A Duesterwald, Evelyn
%A Huo, Siyu
%A Isahagian, Vatche
%A Jayaram, K. R.
%A Kumar, Ritesh
%A Muthusamy, Vinod
%A Oum, Punleuk
%A Saha, Debashish
%A Thomas, Gegi
%A Venkateswaran, Praveen
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F duesterwald-etal-2025-flow
%X Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts.This paper contributes (i) FLOW-BENCH, a high quality dataset of paired NL instructions and business process definitions toevaluate NL-based BPA tools, and support research in this area, and (ii) FLOW-GEN,our approach to utilize LLMs to translate NL into an intermediate Python representation that facilitates final conversion into widely adopted business process definition languages, such as BPMN and DMN. We bootstrap FLOW-BENCH by demonstrating how it can be used to evaluate the components of FLOW-GEN across eight LLMs. We hope that FLOW-GEN and FLOW-BENCHcatalyze further research in BPA.
%U https://aclanthology.org/2025.emnlp-industry.100/
%P 1426-1436
Markdown (Informal)
[FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows](https://aclanthology.org/2025.emnlp-industry.100/) (Duesterwald et al., EMNLP 2025)
ACL
- Evelyn Duesterwald, Siyu Huo, Vatche Isahagian, K. R. Jayaram, Ritesh Kumar, Vinod Muthusamy, Punleuk Oum, Debashish Saha, Gegi Thomas, and Praveen Venkateswaran. 2025. FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1426–1436, Suzhou (China). Association for Computational Linguistics.