Summary of Exploring Combinatorial Problem Solving with Large Language Models: a Case Study on the Travelling Salesman Problem Using Gpt-3.5 Turbo, by Mahmoud Masoud et al.
Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo
by Mahmoud Masoud, Ahmed Abdelhay, Mohammed Elhenawy
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research explores the potential of Large Language Models (LLMs) for solving combinatorial problems like the Travelling Salesman Problem (TSP). The study utilizes GPT-3.5 Turbo and various approaches, including zero-shot in-context learning, few-shot in-context learning, and chain-of-thoughts (CoT). Experiments demonstrate promising performance on problems of similar size to the training instances and good generalization to larger problems. To further improve performance without additional training costs, a self-ensemble approach is employed to enhance solution quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are special types of computers that can generate text based on what they’re told. Researchers have been working on these models for many complex tasks, but not much has been done to see if they can help solve problems like the Travelling Salesman Problem (TSP). In this study, scientists used a powerful model called GPT-3.5 Turbo and different ways of using it to try and solve TSP. They found that the model did well on smaller problems and was good at solving larger ones too! To make it even better without having to train it more, they came up with an idea to use the model in a special way. |
Keywords
» Artificial intelligence » Few shot » Generalization » Gpt » Zero shot
