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.

大型语言模型 (LLMs) 在最近因其出色的理解和推理能力而受到了广泛关注，取得了许多领域的巨大进展。LLM 技术的进步也为电信领域的许多任务自动化提供了有希望的机会。本工作旨在提供 LLM-enabled 电信网络的全面概述，介绍了 LLM 的基本原理、关键技术和电信应用，并给出了未来发展方向。