How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems

arXiv:2403.01757v1 Announce Type: cross
Abstract: Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges. Despite this recognition, a predominant limitation of existing LLM-based optimization methods is their struggle to capture the relationships among decision variables when relying exclusively on numerical text prompts, especially in high-dimensional problems. Keeping this in mind, we first propose to enhance the optimization performance using multimodal LLM capable of processing both textual and visual prompts for deeper insights …

abstract arxiv boost case case study challenges cs.ai cs.cl cs.lg cs.ne decision integration language language models large language large language models llm llms math.oc multimodal optimization performance recognition relationships routing struggle study them tools type variables