Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
<|reference_start|>Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language: 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.<|reference_end|>
@article{faghihi2024prompt2demodel:,
    title={Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural
  Language},
    author={Hossein Rajaby Faghihi, Aliakbar Nafar, Andrzej Uszok, Hamid Karimian,
  and Parisa Kordjamshidi},
    journal={arXiv preprint arXiv:2407.20513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20513},
    primaryClass={cs.CL cs.AI cs.HC}
}