Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners without optimization expertise in various application domains. As a result, practitioners often struggle to interact with and draw useful conclusions from optimization models independently. To fill this gap, we introduce OptiChat, a natural language dialogue system designed to help practitioners interpret model formulation, diagnose infeasibility, analyze sensitivity, retrieve information, evaluate modifications, and provide counterfactual explanations. By augmenting large language models (LLMs) with functional calls and code generation tailored for optimization models, we enable seamless interaction and minimize the risk of hallucinations in OptiChat. We develop a new dataset to evaluate OptiChat's performance in explaining optimization models. Experiments demonstrate that OptiChat effectively bridges the gap between optimization models and practitioners, delivering autonomous, accurate, and instant responses.
@article{arxiv.2501.08406,
title = {OptiChat: Bridging Optimization Models and Practitioners with Large Language Models},
author = {Hao Chen and Gonzalo Esteban Constante-Flores and Krishna Sri Ipsit Mantri and Sai Madhukiran Kompalli and Akshdeep Singh Ahluwalia and Can Li},
journal= {arXiv preprint arXiv:2501.08406},
year = {2025}
}