When Large Language Model Meets Optimization

TL;DR

This paper reviews how integrating large language models with optimization algorithms can improve decision-making, enhance AI capabilities, and address complex problem-solving challenges, highlighting recent progress and future research directions.

Contribution

It provides a comprehensive overview of the synergy between LLMs and optimization algorithms, emphasizing novel approaches and potential for advancing general AI.

Findings

LLMs facilitate intelligent modeling and strategic decision-making.

Optimization algorithms refine LLM architectures and outputs.

The synergy addresses computational challenges in complex problems.

Abstract

Optimization algorithms and large language models (LLMs) enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, with extensive domain knowledge, facilitate intelligent modeling and strategic decision-making in optimization, while optimization algorithms refine LLM architectures and output quality. This synergy offers novel approaches for advancing general AI, addressing both the computational challenges of complex problems and the application of LLMs in practical scenarios. This review outlines the progress and potential of combining LLMs with optimization algorithms, providing insights for future research directions.