README.md

metadata

license: apache-2.0
task_categories:
  - text-generation
  - text2text-generation
tags:
  - code
size_categories:
  - n<1K
language:
  - en

CP-Bench: A dataset for evaluating LLM-driven constraint modelling

This dataset is designed to faciliate the evaluation of LLM-based methods for translating natural language problem descriptions into accurate constraint specifications. It contains diverse combinatorial problems, and is sourced from various well-established sources from the Constraint Programming community.

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📊 Leaderboard

You can submit your results or view others' performance here:
👉 CP-Bench Leaderboard on Hugging Face

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Dataset Breakdown

The dataset contains problems from the following sources:

• aplai_course: Problems from the APLAI course of KU Leuven, 2023-2024. As modelled here.
• cpmpy_examples: Problems from the CPMpy repository
  • All included, except for the ones that require enumeration of all solutions (e.g. solveAll).
• csplib
  • For now, only the ones modelled in the CPMpy repository are included, and the ones modelled by Hakan Kjellerstrand.
• hakan_examples: Models created by Hakan Kjellerstrand
  • In progress with alphabetical order. Currently, includes all problems until crypta.py, excluding the following:
    • Those already modelled from other sources (e.g. aplai_course, cpmpy_examples, csplib)
    • Those that contain solveAll (counting solutions).
    • Global constraints tests, e.g. http://www.hakank.org/cpmpy/atmost_test.py

Diversity

We attempted to include unique problems from different sources, in order to provide a diverse set of problems. However, as this was a manual process, there might be duplicates or similar problems. If you notice any issues, please let us know.

Citation

If you found this dataset useful, please consider citing it as follows:

@dataset{michailidis_2025_15592407,
  author       = {Michailidis, Kostis and
                  Tsouros, Dimosthenis and
                  Guns, Tias},
  title        = {CP-Bench},
  month        = jun,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.15592407},
  url          = {https://doi.org/10.5281/zenodo.15592407},
}