Opt-Verifier：通过双面验证释放大语言模型在优化建模中的潜力
Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
AI总结：
提出Opt-Verifier框架，通过结构侧和解决方案侧的双面验证，利用大语言模型自动构建数学优化模型，显著提升建模准确性。
摘要：
构建数学优化模型在运筹学中至关重要，但需要大量人类专业知识。最近的进展利用大语言模型（LLMs）来自动化这一建模过程。然而，现有工作往往难以验证生成的优化模型的正确性，既不检查约束和变量的合理性，也不检查生成模型解的有效性。这阻碍了后续的验证和纠正步骤，从而严重损害了建模准确性。为了解决这一挑战，我们提出了一种新颖的基于LLM的框架，具有从结构和解决方案两个角度的双面验证（Opt-Verifier），从而提高建模准确性。结构侧验证确保生成的优化模型的建模结构与原始问题描述一致，准确捕捉问题的约束和要求。同时，解决方案侧验证解释和评估解的有效性，确认优化模型在逻辑和数学上是合理的。在流行基准上的实验表明，我们的方法在准确性上提高了20%以上。
英文摘要：
Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language models (LLMs) to automate this modeling process. However, existing works often struggle to verify the correctness of the generated optimization models, without checking the rationality of the constraints and variables or the validity of solutions to the generated models. This hampers the subsequent verification and correction steps, and thus it severely hurts the modeling accuracy. To address this challenge, we propose a novel LLM-based framework with Dual-side Verification (Opt-Verifier) from both structure and solution perspectives, thereby improving the modeling accuracy. The structure-side verification ensures that the modeling structure of the generated optimization models aligns with the original problem description, accurately capturing the problem's constraints and requirements. Meanwhile, the solution-side verification interprets and evaluates the solutions' validity, confirming that the optimization models are logically and mathematically sound. Experiments on popular benchmarks demonstrate that our approach achieves over 20\% improvement in accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
