Uncertainty-Aware Simulation-Based Inference for Operations Research with Large Language Models

Short summary

Researchers propose a training-free, uncertainty-aware inference framework for using LLMs in operations research tasks. The method uses short lookahead simulations to evaluate intermediate formulation steps and dynamically selects candidates likely to yield coherent optimization models. Across benchmarks including NL4OPT, MAMO, and IndustryOR, the framework consistently outperforms standard and low-temperature baselines.

• •Training-free framework for LLM-based OR mathematical modeling
• •Uses lookahead simulations to quantify downstream uncertainty and resample candidates
• •Outperforms baselines on NL4OPT, MAMO, and IndustryOR benchmarks

Generated with AI, which can make mistakes.