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This paper highlights the challenges of applying large-scale language models (LLMs) to operational research (OR) problems—the lack of self-correction and the complexity of expert selection—and presents ORMind, a novel framework to address these challenges. ORMind implements an end-to-end workflow that translates requirements into mathematical models and executable solver code using counterfactual reasoning, and is being internally tested on Lenovo's AI assistant. Experimental results show that ORMind achieves a 9.5% performance improvement on the NL4Opt dataset and a 14.6% performance improvement on the ComplexOR dataset.
Takeaways, Limitations
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Takeaways:
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We clearly present the practical challenges of solving operations research problems using LLM and propose a novel approach (ORMind) to address them.
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ORMind demonstrates improved performance over existing methods, suggesting the potential to enhance the practical utility of LLM-based operations research.
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It is applied to Lenovo's AI assistant and demonstrates its potential for use in real-world industrial settings.
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Limitations:
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Currently, it is only being tested internally at Lenovo, and generalization performance on external datasets and various OR problems requires further validation.
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The performance improvements presented in the paper are results for a specific dataset, and it is unclear whether they apply equally to all types of OR problems.
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There is a lack of detailed description of ORMind's specific algorithms and implementation details.
