# Towards Automatic Composition of ASP Programs from Natural Language Specifications

arXiv: https://arxiv.org/abs/2403.04541 · HTML: https://mlsanity.com/paper/2403.04541/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2107.02794/) (2021)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text](https://mlsanity.com/paper/2307.07696/) (2023)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://mlsanity.com/paper/1808.06945/) (2018)
- [QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers](https://mlsanity.com/paper/2202.00120/) (2022)
- [PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation](https://mlsanity.com/paper/2010.02301/) (2020)
- [Leveraging Large Language Models to Generate Answer Set Programs](https://mlsanity.com/paper/2307.07699/) (2023)
- [A Survey and Classification of Controlled Natural Languages](https://mlsanity.com/paper/1507.01701/) (2015)
- [AI-driven Development Is Here: Should You Worry?](https://mlsanity.com/paper/2204.07560/) (2022)
- [The Seventh Answer Set Programming Competition: Design and Results](https://mlsanity.com/paper/1904.09134/) (2019)
- [CNL2ASP: converting controlled natural language sentences into ASP](https://mlsanity.com/paper/2311.10505/) (2023)
- [Specifying and Verbalising Answer Set Programs in Controlled Natural Language](https://mlsanity.com/paper/1804.10765/) (2018)
- [Clingraph: A System for ASP-based Visualization](https://mlsanity.com/paper/2303.10118/) (2023)

## Built on by (most cited first, 7 of 7)

- [LLASP: Fine-tuning Large Language Models for Answer Set Programming](https://mlsanity.com/paper/2407.18723/) (2024)
- [Can LLMs Solve ASP Problems? Insights from a Benchmarking Study (Extended Version)](https://mlsanity.com/paper/2507.19749/) (2025)
- [Bridging Natural Language and ASP: A Hybrid Approach Using LLMs and AMR Parsing](https://mlsanity.com/paper/2511.08715/) (2025)
- [XAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisions](https://mlsanity.com/paper/2601.03844/) (2026)
- [ASP-Bench: From Natural Language to Logic Programs](https://mlsanity.com/paper/2602.01171/) (2026)
- [Streamliners for Answer Set Programming](https://mlsanity.com/paper/2604.19251/) (2026)
- [BLAST: Benchmarking LLMs with ASP-based Structured Testing](https://mlsanity.com/paper/2604.22306/) (2026)

## Abstract (arXiv)

This paper moves the first step towards automating the composition of Answer Set Programming (ASP) specifications. In particular, the following contributions are provided: (i) A dataset focused on graph-related problem specifications, designed to develop and assess tools for ASP automatic coding; (ii) A two-step architecture, implemented in the NL2ASP tool, for generating ASP programs from natural language specifications. NL2ASP uses neural machine translation to transform natural language into Controlled Natural Language (CNL) statements. Subsequently, CNL statements are converted into ASP code using the CNL2ASP tool. An experiment confirms the viability of the approach.