From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto

TL;DR

This paper envisions a Decision Optimization CoPilot leveraging Large Language Models to simplify and enhance the creation of optimization models for real-world decision-making, highlighting current capabilities and future research directions.

Contribution

It introduces the concept of a Decision Optimization CoPilot that integrates LLMs with optimization, outlining fundamental requirements and proposing research directions.

Findings

LLMs already offer significant capabilities for optimization tasks.

Major research challenges in integrating LLMs with optimization remain.

A call for collaboration between LLM and optimization communities.

Abstract

Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at the intersection of LLMs and optimization to create a Decision Optimization CoPilot (DOCP) - an AI tool designed to assist any decision maker, interacting in natural language to grasp the business problem, subsequently formulating and solving the corresponding optimization model. This paper outlines our DOCP vision and identifies several fundamental requirements for its implementation. We describe the state of the art through a literature survey and experiments using ChatGPT. We show that a) LLMs already provide substantial novel capabilities relevant to a DOCP, and b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We also see this work as a call to action to bring together the LLM and optimization communities to pursue our vision, thereby enabling much more widespread improved decision-making.