# Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem

arXiv: https://arxiv.org/abs/2308.15890 · HTML: https://mlsanity.com/paper/2308.15890/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch](https://mlsanity.com/paper/2109.08291/) (2021)

## Built on by (most cited first, 1 of 1)

- [Explainable Belief Harmonization under Dynamic Epistemic Partitions](https://mlsanity.com/paper/2607.21210/) (2026)

## Abstract (arXiv)

Driven by expressiveness commonalities of Python and our Python-based embedded logic-based language Natlog, we design high-level interaction patterns between equivalent language constructs and data types on the two sides. By directly connecting generators and backtracking, nested tuples and terms, coroutines and first-class logic engines, reflection and meta-interpretation, we enable logic-based language constructs to access the full power of the Python ecosystem. We show the effectiveness of our design via Natlog apps working as orchestrators for JAX and Pytorch pipelines and as DCG-driven GPT3 and DALL.E prompt generators. Keyphrases: embedding of logic programming in the Python ecosystem, high-level inter-paradigm data exchanges, coroutining with logic engines, logic-based neuro-symbolic computing, logic grammars as prompt-generators for Large Language Models, logic-based neural network configuration and training.