As the dawn of sixth-generation (6G) networking approaches, it promises unprecedented advancements in communication and automation. Among the leading innovations of 6G is the concept of Zero Touch Networks (ZTNs), aiming to achieve fully automated, self-optimizing networks with minimal human intervention. Despite the advantages ZTNs offer in terms of efficiency and scalability, challenges surrounding transparency, adaptability, and human trust remain prevalent. Concurrently, the advent of Large Language Models (LLMs) presents an opportunity to elevate the ZTN framework by bridging the gap between automated processes and human-centric interfaces. This paper explores the integration of LLMs into ZTNs, highlighting their potential to enhance network transparency and improve user interactions. Through a comprehensive case study on deep reinforcement learning (DRL)-based anti-jamming technique, we demonstrate how LLMs can distill intricate network operations into intuitive, human-readable reports. Additionally, we address the technical and ethical intricacies of melding LLMs with ZTNs, with an emphasis on data privacy, transparency, and bias reduction. Looking ahead, we identify emerging research avenues at the nexus of LLMs and ZTNs, advocating for sustained innovation and interdisciplinary synergy in the domain of automated networks.

翻译：随着第六代（6G）网络时代的临近，其承诺在通信与自动化领域带来前所未有的进步。6G的核心创新之一是零接触网络（ZTN）概念，旨在实现完全自动化、自我优化的网络，并最大限度减少人工干预。尽管ZTN在效率和可扩展性方面具有优势，但围绕透明度、适应性和人类信任的挑战依然普遍存在。与此同时，大语言模型（LLM）的出现为通过弥合自动化流程与人机交互界面之间的差距来提升ZTN框架提供了契机。本文探讨了将LLM集成到ZTN中的方案，重点阐述了其增强网络透明度及改善用户交互的潜力。通过一个关于基于深度强化学习（DRL）抗干扰技术的综合案例研究，我们展示了LLM如何将复杂的网络操作提炼为直观、可读性高的人工报告。此外，我们探讨了将LLM与ZTN融合所涉及的技术与伦理复杂性，特别关注数据隐私、透明度及偏差减少。展望未来，我们识别了LLM与ZTN交叉领域新兴的研究方向，倡导在自动化网络领域持续创新并推动跨学科协同。