OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
This addresses the gap for practitioners in various domains who need to use optimization models but lack expertise, though it is incremental as it builds on existing LLM and optimization techniques.
The paper tackles the problem of practitioners without optimization expertise struggling to interact with optimization models by introducing OptiChat, a natural language dialogue system that helps interpret, diagnose, and analyze these models, with experiments showing it delivers autonomous, accurate, and instant responses.
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.