mlsanityData to 7 Oct 2026

Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language

arXiv 2407.20513 ↗First posted 30 Jul 2024cs.CL cs.AI cs.HCcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 47 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

36 of 47 references.

References · 36

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalizationcited by 13
  2. 2025Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysiscited by 2
  3. 2026An Agentic Framework for Neuro-Symbolic Programmingcited by 1