Most business process automation is still developed using traditional
automation technologies such as workflow engines. These systems provide domain
specific languages that require both business knowledge and programming skills
to effectively use. As such, business users often lack adequate programming
skills to fully leverage these code oriented environments. We propose a
paradigm for the construction of business automations using natural language.
The approach applies a large language model to translate business rules and
automations described in natural language, into a domain specific language
interpretable by a business rule engine. We compare the performance of various
language model configurations, across various target domains, and explore the
use of constrained decoding to ensure syntactically correct generation of
output.

本研究提出了一种基于自然语言处理的业务自动化构建范例，通过应用大型自然语言模型将业务规则和自动化描述转换为业务规则引擎可解释的领域特定语言，以解决业务用户缺乏足够的编程能力的问题。实验比较了不同语言模型配置在不同目标领域下的性能，并探索了应用约束解码以确保正确生成输出的可能性。