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This is a Experimental Study study.

September 20, 2025

Guiding Large Language Models in Modeling Optimization Problems via Question Partitioning

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Our method achieves an accuracy of 62.3% in modeling optimization problems from natural language descriptions.
Using the NLP4LP benchmark dataset, we demonstrate a code executability rate of 86.8% when tested on GPT-4.
The PaMOP framework utilizes a tree structure to partition problems, improving LLM efficiency in constraint modeling.
Results indicate that automating model generation can significantly address inefficiencies in traditional optimization problem-solving.

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This framework enhances accuracy in solving optimization problems using large language models, highlighting new approaches to complex constraints.

Pan et al. (2025) studied this question.

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This framework enhances accuracy in solving optimization problems using large language models, highlighting new approaches to complex constraints.