Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities to automate many tasks in the telecommunication (telecom) field. After pre-training and fine-tuning, LLMs can perform diverse downstream tasks based on human instructions, paving the way to artificial general intelligence (AGI)-enabled 6G. Given the great potential of LLM technologies, this work aims to provide a comprehensive overview of LLM-enabled telecom networks. In particular, we first present LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment. Then, we introduce LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems. Specifically, the LLM-enabled generation applications include telecom domain knowledge, code, and network configuration generation. After that, the LLM-based classification applications involve network security, text, image, and traffic classification problems. Moreover, multiple LLM-enabled optimization techniques are introduced, such as automated reward function design for reinforcement learning and verbal reinforcement learning. Furthermore, for LLM-aided prediction problems, we discussed time-series prediction models and multi-modality prediction problems for telecom. Finally, we highlight the challenges and identify the future directions of LLM-enabled telecom networks.

翻译：大语言模型（LLMs）因其卓越的理解与推理能力而受到广泛关注，并在多个领域取得了长足进展。LLM技术的进步也为自动化电信领域的多项任务带来了广阔前景。通过预训练与微调，LLMs能基于人类指令执行多样化的下游任务，为迈向通用人工智能（AGI）赋能的6G铺平道路。鉴于LLM技术的巨大潜力，本文旨在全面综述基于LLM的电信网络。具体而言，我们首先介绍LLM基础理论，涵盖模型架构、预训练、微调、推理与利用、模型评估及电信部署。随后，我们阐述了LLM赋能的关键技术及其在生成、分类、优化和预测问题中的电信应用。其中，LLM赋能的生成应用包括电信领域知识生成、代码生成及网络配置生成。其次，基于LLM的分类应用涉及网络安全、文本、图像及流量分类问题。此外，本文介绍了多种LLM赋能的优化技术，例如面向强化学习的自动奖励函数设计与言语强化学习。进一步地，针对LLM辅助的预测问题，我们讨论了时间序列预测模型及电信多模态预测。最后，我们总结了LLM赋能电信网络面临的挑战，并指出了未来研究方向。