用大模型自动把自然语言转为随机优化模型，提升建模效率。
Large Language Model-Based Automatic Formulation for Stochastic Optimization Models
- 设计结构化提示词，引导大模型分步推理生成优化模型。
- GPT-4-Turbo在多数问题上生成模型的结构质量更高，部分正确率提升显著。
- 适合需要快速建模的工程师或研究者，推动语言驱动的智能建模流程。
本文系统研究大语言模型（特别是ChatGPT）在从自然语言描述自动生成和求解随机优化（SO）问题方面的表现。聚焦三类关键模型：个体机会约束模型、联合机会约束模型以及两阶段随机混合整数线性规划模型。我们设计了多种提示词，通过思维链和代理式推理引导模型完成结构化任务。提出一种新颖的软评分指标，评估生成模型的结构质量和部分正确性，克服传统准确率度量的局限。在多样化的SO问题上，GPT-4-Turbo在除个体机会约束问题外均优于GPT-3.5系列。结构化提示显著优于简单提示，减少冗余变量生成并提升目标函数匹配度，但冗余生成仍是挑战。研究发现，通过精心设计的提示与多代理协作，大模型可有效辅助随机优化建模，为实际应用中语言驱动的智能化建模流程铺平道路。
原文摘要 · Abstract (English)
This paper presents an integrated systematic study of the performance of large language models (LLMs), specifically ChatGPT, for automatically formulating and solving Stochastic Optimization (SO) problems from natural language descriptions. Focusing on three key categories, individual chance-constrained models, joint chance-constrained models, and two-stage stochastic mixed-integer linear programming models, we design several prompts that guide ChatGPT through structured tasks using chain-of-thought and agentic reasoning. We introduce a novel soft-scoring metric that evaluates the structural quality and partial correctness of generated models, addressing the limitations of canonical and execution-based accuracy metrics. Across a diverse set of SO problems, GPT-4-Turbo achieves better partial scores than GPT-3.5 variants except for individual chance-constrained problems. Structured prompts significantly outperform simple prompting, reducing extra-element generation and improving objective matching, although extra-element generation remains a nontrivial task. Our findings reveal that with well-engineered prompts and multi-agent collaboration, LLMs can facilitate SO formulations, paving the way for intelligent, language-driven modeling pipelines for SO in practice.
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