Summary of Eyeballing Combinatorial Problems: a Case Study Of Using Multimodal Large Language Models to Solve Traveling Salesman Problems, by Mohammed Elhenawy et al.
Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems
by Mohammed Elhenawy, Ahmed Abdelhay, Taqwa I. Alhadidi, Huthaifa I Ashqar, Shadi Jaradat, Ahmed Jaber, Sebastien Glaser, Andry Rakotonirainy
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multimodal Large Language Models (MLLMs) have shown impressive capabilities in processing diverse modalities, including text, images, and audio. This paper explores the use of MLLMs’ visual capabilities to solve complex problems like the Traveling Salesman Problem (TSP). By analyzing images of point distributions on a two-dimensional plane, the authors investigate whether MLLMs can effectively identify viable TSP routes without explicit training data. The results from zero-shot, few-shot, self-ensemble, and self-refine evaluations demonstrate promising outcomes. This study highlights the potential for MLLMs to tackle combinatorial problems through visual reasoning. Key findings include the use of multimodal language models for solving TSP, leveraging their capabilities in few-shot and zero-shot learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a problem that’s hard to solve. This paper explores how computers can look at pictures and use this information to find answers without needing lots of practice or training data. They’re trying to figure out if these smart computers can use images to help solve big problems like finding the shortest route for a traveling salesperson. The results show that these computers are pretty good at solving this problem, which could lead to new ways to tackle similar challenges. |
Keywords
» Artificial intelligence » Few shot » Zero shot
