用自然语言生成企业工作流，提升自动化开发效率。
FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows
- 构建可评估的自然语言到工作流转换数据集
- 通过Python中间表示实现高精度工作流生成
- 适合想快速上手业务流程自动化的开发者
利用大语言模型（LLMs）将自然语言指令转化为结构化业务流程成果，正成为热门研究方向。本文提出两项技术贡献：(i) FLOW-BENCH，一个高质量的自然语言指令与结构化业务流程定义配对数据集，用于评估基于自然语言的业务流程自动化（BPA）工具，并推动该领域研究发展；(ii) FLOW-GEN，一种利用LLMs将自然语言翻译为具有Python语法的中间表示的方法，便于最终转换为广泛采用的业务流程定义语言（如BPMN和DMN）。我们通过展示如何使用FLOW-BENCH评估八种不同规模的LLM在FLOW-GEN各组件上的表现，验证了其有效性。期望FLOW-GEN与FLOW-BENCH能进一步促进业务流程自动化研究，让新手与专家用户更易参与。
原文摘要 · Abstract (English)
Business process automation (BPA) that leverages Large Language Models (LLMs) to convert natural language (NL) instructions into structured business process artifacts is becoming a hot research topic. This paper makes two technical contributions -- (i) FLOW-BENCH, a high quality dataset of paired natural language instructions and structured business process definitions to evaluate NL-based BPA tools, and support bourgeoning research in this area, and (ii) FLOW-GEN, our approach to utilize LLMs to translate natural language into an intermediate representation with Python syntax 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 of varying sizes. We hope that FLOW-GEN and FLOW-BENCH catalyze further research in BPA making it more accessible to novice and expert users.
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