Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc

• URL: http://arxiv.org/abs/2503.10642v1
• Date: Sat, 22 Feb 2025 04:13:53 GMT
• Title: Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc
• Authors: Akash Singirikonda, Serdar Kadioglu, Karthik Uppuluri,
• Abstract summary: This paper introduces Text2Zinc, a cross-domain dataset for capturing optimization and satisfaction problems specified in natural language text.<n>Our work is distinguished from previous attempts by integrating both satisfaction and optimization problems within a unified dataset.<n>Our findings indicate that LLMs are not yet a push-button technology to model problems from text.
• Score: 2.6217304977339473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is growing interest in utilizing large language models (LLMs) as co-pilots for combinatorial optimization and constraint programming tasks across various problems. This paper aims to advance this line of research by introducing Text2Zinc}, a cross-domain dataset for capturing optimization and satisfaction problems specified in natural language text. Our work is distinguished from previous attempts by integrating both satisfaction and optimization problems within a unified dataset using a solver-agnostic modeling language. To achieve this, we leverage MiniZinc's solver-and-paradigm-agnostic modeling capabilities to formulate these problems. Using the Text2Zinc dataset, we conduct comprehensive baseline experiments to compare execution and solution accuracy across several methods, including off-the-shelf prompting strategies, chain-of-thought reasoning, and a compositional approach. Additionally, we explore the effectiveness of intermediary representations, specifically knowledge graphs. Our findings indicate that LLMs are not yet a push-button technology to model combinatorial problems from text. We hope that Text2Zinc serves as a valuable resource for researchers and practitioners to advance the field further.

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