Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo

Understand

Large Language Models (LLMs) are deep learning models designed to generate text based on textual input.

• Although researchers have been developing these models for more complex tasks such as code generation and general reasoning, few efforts have explored how LLMs can be applied to combinatorial problems.
• In this research, we investigate the potential of LLMs to solve the Travelling Salesman Problem (TSP).
• Utilizing GPT-3.5 Turbo, we conducted experiments employing various approaches, including zero-shot in-context learning, few-shot in-context learning, and chain-of-thoughts (CoT).