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Bridging Natural Language and ASP: A Hybrid Approach Using LLMs and AMR Parsing

arXiv 2511.08715 ↗First posted 11 Nov 2025cs.AIcited by 0

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Abstract (arXiv)

Answer Set Programming (ASP) is a declarative programming paradigm based on logic programming and non-monotonic reasoning. It is a tremendously powerful tool for describing and solving combinatorial problems. Like any other language, ASP requires users to learn how it works and the syntax involved. It is becoming increasingly required for those unfamiliar with programming languages to interact with code. This paper proposes a novel method of translating unconstrained English into ASP programs for logic puzzles using an LLM and Abstract Meaning Representation (AMR) graphs. Everything from ASP rules, facts, and constraints is generated to fully represent and solve the desired problem. Example logic puzzles are used to demonstrate the capabilities of the system. While most current methods rely entirely on an LLM, our system minimizes the role of the LLM only to complete straightforward tasks. The LLM is used to simplify natural language sentences, identify keywords, and generate simple facts. The AMR graphs are then parsed from simplified language and used to generate ASP constraints systematically. The system successfully creates an entire ASP program that solves a combinatorial logic problem. This approach is a significant first step in creating a lighter-weight, explainable system that converts natural language to solve complex logic problems.

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References · 17

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  5. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  6. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 391
  7. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Proverscited by 168
  8. 2023Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Modelscited by 37
  9. 2023Leveraging Large Language Models to Generate Answer Set Programscited by 33
  10. 2023Reliable Natural Language Understanding with Large Language Models and Answer Set Programmingcited by 12
  11. 2024LLASP: Fine-tuning Large Language Models for Answer Set Programmingcited by 8
  12. 2024Towards Automatic Composition of ASP Programs from Natural Language Specificationscited by 7
5 more
  1. 2024Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMscited by 6
  2. 2023CNL2ASP: converting controlled natural language sentences into ASPcited by 5
  3. 2025Logic-of-Thought: Empowering Large Language Models with Logic Programs for Solving Puzzles in Natural Languagecited by 5
  4. 2025Question Answering with LLMs and Learning from Answer Setscited by 4
  5. 2025An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoningcited by 3

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