# CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models

arXiv: https://arxiv.org/abs/2509.07867 · HTML: https://mlsanity.com/paper/2509.07867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions](https://mlsanity.com/paper/2303.08233/) (2023)
- [Formal Specifications from Natural Language](https://mlsanity.com/paper/2206.01962/) (2022)
- [Holy Grail 2.0: From Natural Language to Constraint Models](https://mlsanity.com/paper/2308.01589/) (2023)
- [MCP-Solver: Integrating Language Models with Constraint Programming Systems](https://mlsanity.com/paper/2501.00539/) (2024)
- [Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems](https://mlsanity.com/paper/2212.13201/) (2022)
- [OptiChat: Bridging Optimization Models and Practitioners with Large Language Models](https://mlsanity.com/paper/2501.08406/) (2025)
- [XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems](https://mlsanity.com/paper/1611.03398/) (2016)
- [A Novel Approach for Auto-Formulation of Optimization Problems](https://mlsanity.com/paper/2302.04643/) (2023)
- [Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description](https://mlsanity.com/paper/2212.03575/) (2022)
- [Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentations](https://mlsanity.com/paper/2212.14657/) (2022)
- [Proceedings of the 2024 XCSP3 Competition](https://mlsanity.com/paper/2412.00117/) (2024)

## Abstract (arXiv)

Constraint Programming and its high-level modeling languages have long been recognized for their potential to achieve the holy grail of problem-solving. However, the complexity of modeling languages, the large number of global constraints, and the art of creating good models have often hindered non-experts from choosing CP to solve their combinatorial problems. While generating an expert-level model from a natural-language description of a problem would be the dream, we are not yet there. We propose a tutoring system called CP-Model-Zoo, exploiting expert-written models accumulated through the years. CP-Model-Zoo retrieves the closest source code model from a database based on a user's natural language description of a combinatorial problem. It ensures that expert-validated models are presented to the user while eliminating the need for human data labeling. Our experiments show excellent accuracy in retrieving the correct model based on a user-input description of a problem simulated with different levels of expertise.