Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929441611972608 |
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| author | Faghihi, Hossein Rajaby Nafar, Aliakbar Uszok, Andrzej Karimian, Hamid Kordjamshidi, Parisa |
| author_facet | Faghihi, Hossein Rajaby Nafar, Aliakbar Uszok, Andrzej Karimian, Hamid Kordjamshidi, Parisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language Faghihi, Hossein Rajaby Nafar, Aliakbar Uszok, Andrzej Karimian, Hamid Kordjamshidi, Parisa Computation and Language Artificial Intelligence Human-Computer Interaction 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. |
| title | Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.20513 |