Summary of Prompt2demodel: Declarative Neuro-symbolic Modeling with Natural Language, by Hossein Rajaby Faghihi et al.
Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
by Hossein Rajaby Faghihi, Aliakbar Nafar, Andrzej Uszok, Hamid Karimian, Parisa Kordjamshidi
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A conversational pipeline is proposed for crafting domain knowledge for complex neuro-symbolic models through natural language prompts. The pipeline leverages large language models to generate declarative programs in the DomiKnowS framework, which expresses concepts and their relationships as a graph with logical constraints. Techniques like dynamic in-context demonstration retrieval, model refinement based on feedback from a symbolic parser, visualization, and user interaction are used to generate task structure and formal knowledge representation. This approach enables domain experts without extensive ML/AI knowledge to formally declare their knowledge for incorporation into customized neural models in the DomiKnowS framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to help experts add information to special kinds of AI models called neuro-symbolic models. These models are very good at understanding and making decisions about complex things, but they need information from different areas to work well. The new approach uses big language models to create simple instructions that can be used by the neuro-symbolic models. This makes it easier for experts who don’t know much about AI or machine learning to add their knowledge to these special models. |
Keywords
» Artificial intelligence » Machine learning
