@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://preview.aclanthology.org/ingest-emnlp/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."
}