  This paper presents a conversational pipeline for crafting domain knowledge
for complex neuro-symbolic models through natural language prompts. It
leverages large language models to generate declarative programs in the
DomiKnowS framework. The programs in this framework express concepts and their
relationships as a graph in addition to logical constraints between them. The
graph, later, can be connected to trainable neural models according to those
specifications. Our proposed pipeline utilizes techniques like dynamic
in-context demonstration retrieval, model refinement based on feedback from a
symbolic parser, visualization, and user interaction to generate the tasks'
structure and formal knowledge representation. This approach empowers domain
experts, even those not well-versed in ML/AI, to formally declare their
knowledge to be incorporated in customized neural models in the DomiKnowS
framework.
