GeNet：基于多模态大语言模型的网络拓扑与配置协同助手
GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration
AI总结：
针对企业网络工程手动流程复杂且现有自动化研究忽视拓扑变化的问题，本文提出GeNet多模态协同助手框架，通过LLM结合视觉与文本模态处理拓扑和配置，在Cisco场景中验证了其有效性并强调拓扑理解的重要性。
摘要：
企业环境中的通信网络工程传统上是复杂、耗时且易出错的手动流程。大多数网络工程自动化研究集中在配置合成上，往往忽视物理网络拓扑的变化。本文介绍GeNet，一种面向企业网络工程师的多模态协同助手。GeNet是一种新颖框架，利用大语言模型（LLM）简化网络设计工作流。它使用视觉和文本模态，基于用户意图解释和更新网络拓扑及设备配置。GeNet在改编自Cisco认证练习的企业网络场景中进行了评估。结果表明，GeNet能准确解释网络拓扑图像，可能减少网络工程师的工作量并加速企业环境中的网络设计流程。此外，我们展示了在处理需要修改网络拓扑的意图时，精确理解拓扑的重要性。
英文摘要：
Communication network engineering in enterprise environments is traditionally a complex, time-consuming, and error-prone manual process. Most research on network engineering automation has concentrated on configuration synthesis, often overlooking changes in the physical network topology. This paper introduces GeNet, a multimodal co-pilot for enterprise network engineers. GeNet is a novel framework that leverages a large language model (LLM) to streamline network design workflows. It uses visual and textual modalities to interpret and update network topologies and device configurations based on user intents. GeNet was evaluated on enterprise network scenarios adapted from Cisco certification exercises. Our results demonstrate GeNet's ability to interpret network topology images accurately, potentially reducing network engineers' efforts and accelerating network design processes in enterprise environments. Furthermore, we show the importance of precise topology understanding when handling intents that require modifications to the network's topology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
