Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language

• URL: http://arxiv.org/abs/2407.20513v1
• Date: Tue, 30 Jul 2024 03:10:30 GMT
• Title: Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
• Authors: Hossein Rajaby Faghihi, Aliakbar Nafar, Andrzej Uszok, Hamid Karimian, Parisa Kordjamshidi,
• Abstract summary: This paper presents a pipeline for crafting domain knowledge for complex neuro-symbolic models through natural language prompts. Our proposed pipeline utilizes techniques like dynamic in-context demonstration retrieval, model refinement based on feedback from a symbolic visualization, and user interaction. 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.
• Score: 18.00674366843745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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