Large Language Model-Based Automatic Formulation for Stochastic Optimization Models

• URL: http://arxiv.org/abs/2508.17200v1
• Date: Sun, 24 Aug 2025 03:31:25 GMT
• Title: Large Language Model-Based Automatic Formulation for Stochastic Optimization Models
• Authors: Amirreza Talebi,
• Abstract summary: This paper presents the first integrated systematic study on the performance of large language models (LLMs)<n>We design several prompts that guide ChatGPT through structured tasks using chain-of- thought and modular reasoning.<n>Across a diverse set of problems, GPT-4-Turbo outperforms other models in partial score, variable matching, objective accuracy, with cot_s_instructions emerging as the most effective prompting strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents the first integrated systematic study on the performance of large language models (LLMs), specifically ChatGPT, to automatically formulate and solve stochastic optimiza- tion problems from natural language descriptions. Focusing on three key categories, joint chance- constrained models, individual chance-constrained models, and two-stage stochastic linear programs (SLP-2), we design several prompts that guide ChatGPT through structured tasks using chain-of- thought and modular reasoning. We introduce a novel soft scoring metric that evaluates the struc- tural quality and partial correctness of generated models, addressing the limitations of canonical and execution-based accuracy. Across a diverse set of stochastic problems, GPT-4-Turbo outperforms other models in partial score, variable matching, and objective accuracy, with cot_s_instructions and agentic emerging as the most effective prompting strategies. Our findings reveal that with well-engineered prompts and multi-agent collaboration, LLMs can facilitate specially stochastic formulations, paving the way for intelligent, language-driven modeling pipelines in stochastic opti- mization.

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