A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language
This addresses the accessibility gap in business process automation for non-technical users, though it appears incremental as it builds on existing large language models and domain-specific languages.
The authors tackled the problem of business users lacking programming skills for automation by proposing a natural language approach to author business automations, achieving performance comparisons across various language model configurations and domains.
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.