Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems
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arXiv
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| Format: | Preprint |
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2024
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| author | Elhenawy, Mohammed Abdelhay, Ahmed Alhadidi, Taqwa I. Ashqar, Huthaifa I Jaradat, Shadi Jaber, Ahmed Glaser, Sebastien Rakotonirainy, Andry |
| author_facet | Elhenawy, Mohammed Abdelhay, Ahmed Alhadidi, Taqwa I. Ashqar, Huthaifa I Jaradat, Shadi Jaber, Ahmed Glaser, Sebastien Rakotonirainy, Andry |
| contents | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in processing di-verse modalities, including text, images, and audio. These models leverage extensive pre-existing knowledge, enabling them to address complex problems with minimal to no specific training examples, as evidenced in few-shot and zero-shot in-context learning scenarios. This paper investigates the use of MLLMs' visual capabilities to 'eyeball' solutions for the Traveling Salesman Problem (TSP) by analyzing images of point distributions on a two-dimensional plane. Our experiments aimed to validate the hypothesis that MLLMs can effectively 'eyeball' viable TSP routes. The results from zero-shot, few-shot, self-ensemble, and self-refine zero-shot evaluations show promising outcomes. We anticipate that these findings will inspire further exploration into MLLMs' visual reasoning abilities to tackle other combinatorial problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems Elhenawy, Mohammed Abdelhay, Ahmed Alhadidi, Taqwa I. Ashqar, Huthaifa I Jaradat, Shadi Jaber, Ahmed Glaser, Sebastien Rakotonirainy, Andry Artificial Intelligence Multimodal Large Language Models (MLLMs) have demonstrated proficiency in processing di-verse modalities, including text, images, and audio. These models leverage extensive pre-existing knowledge, enabling them to address complex problems with minimal to no specific training examples, as evidenced in few-shot and zero-shot in-context learning scenarios. This paper investigates the use of MLLMs' visual capabilities to 'eyeball' solutions for the Traveling Salesman Problem (TSP) by analyzing images of point distributions on a two-dimensional plane. Our experiments aimed to validate the hypothesis that MLLMs can effectively 'eyeball' viable TSP routes. The results from zero-shot, few-shot, self-ensemble, and self-refine zero-shot evaluations show promising outcomes. We anticipate that these findings will inspire further exploration into MLLMs' visual reasoning abilities to tackle other combinatorial problems. |
| title | Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.06865 |