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

arXiv:2405.01997v1 Announce Type: new
Abstract: 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, …

abstract arxiv case case study code code generation cs.ai cs.cl deep learning general generate gpt gpt-3 gpt-3.5 language language models large language large language models llms reasoning researchers study tasks text textual turbo type