This repository contains the code for the paper "Constraint Modelling with LLMs using In-Context Learning". The paper explores the potential of using pre-trained Large Language Models (LLMs) to transform textual combinatorial problem descriptions into concrete and executable Constraint Programming (CP) specifications through retrieval-augmented in-context learning. For more details, please refer to the paper.

The repository is structured as follows:

• llms4cp/: Contains the code for the pipeline presented in the paper.
• data/: Contains the datasets used (for more details check there).
• results/: Contains results from the experiments.

• Python 3.9

1. Clone the repository:

   git clone https://github.com/kostis-init/LLM-CP-Modeling.git
2. Navigate to the cloned directory and install dependencies (preferably in a virtual environment):

   pip install -r requirements.txt

Open the configuration file and set the following:

• API keys for LLM platforms (e.g., OPENAI_API_KEY).
  • Only one key is required, depending on the LLM used. For example, for OpenAI's models fill only OPENAI_API_KEY.
• MODEL: Which LLM to use for generating the CP models (e.g., gpt-3.5-turbo).
• NUM_EXAMPLES: Number of examples to add to the context (e.g., 4).
• EXAMPLES_SELECTOR: Method for selecting examples (e.g., static).

Run the program with the following command, specifying the dataset and method:

python main.py --dataset <dataset> --method <method>

For example, to run the program with the APLAI dataset and the CPMPY method, use the following command:

python main.py --dataset APLAI --method CPMPY

If our research is helpful for your work, please consider citing our paper as follows:

@InProceedings{michailidis_et_al:LIPIcs.CP.2024.20,
  author =	{Michailidis, Kostis and Tsouros, Dimos and Guns, Tias},
  title =	{{Constraint Modelling with LLMs Using In-Context Learning}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{20:1--20:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.20},
  URN =		{urn:nbn:de:0030-drops-207053},
  doi =		{10.4230/LIPIcs.CP.2024.20},
  annote =	{Keywords: Constraint Modelling, Constraint Acquisition, Constraint Programming, Large Language Models, In-Context Learning, Natural Language Processing, Named Entity Recognition, Retrieval-Augmented Generation, Optimisation}
}