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Constraint Modelling with LLMs Using In-Context Learning

Authors Kostis Michailidis , Dimos Tsouros , Tias Guns

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Author Details

Kostis Michailidis
  • DTAI, KU Leuven, Belgium
Dimos Tsouros
  • DTAI, KU Leuven, Belgium
Tias Guns
  • DTAI, KU Leuven, Belgium

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant No. 101002802, CHAT-Opt).

Acknowledgements

We want to thank the reviewers for their valuable feedback.

Cite As Get BibTex

Kostis Michailidis, Dimos Tsouros, and Tias Guns. Constraint Modelling with LLMs Using In-Context Learning. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 20:1-20:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.CP.2024.20

Abstract

Constraint Programming (CP) allows for the modelling and solving of a wide range of combinatorial problems. However, modelling such problems using constraints over decision variables still requires significant expertise, both in conceptual thinking and syntactic use of modelling languages. In this work, we explore the potential of using pre-trained Large Language Models (LLMs) as coding assistants, to transform textual problem descriptions into concrete and executable CP specifications. We present different transformation pipelines with explicit intermediate representations, and we investigate the potential benefit of various retrieval-augmented example selection strategies for in-context learning. We evaluate our approach on 2 datasets from the literature, namely NL4Opt (optimisation) and Logic Grid Puzzles (satisfaction), and a heterogeneous set of exercises from a CP course. The results show that pre-trained LLMs have promising potential for initialising the modelling process, with retrieval-augmented in-context learning significantly enhancing their modelling capabilities.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Natural language generation
  • Computing methodologies → Discrete space search
Keywords
  • Constraint Modelling
  • Constraint Acquisition
  • Constraint Programming
  • Large Language Models
  • In-Context Learning
  • Natural Language Processing
  • Named Entity Recognition
  • Retrieval-Augmented Generation
  • Optimisation

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Supplementary Materials

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