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Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui, Yili Jin, Can Chen, Haolun Wu, Dun Yuan, Li Jiang, Di Wu, Xue Liu, Charlie Zhang, Xianbin Wang, Jiangchuan Liu

TL;DR

This work presents LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment, and introduces LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems.

Abstract

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.

Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

TL;DR

This work presents LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment, and introduces LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems.

Abstract

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.
Paper Structure (83 sections, 2 equations, 21 figures, 12 tables)

This paper contains 83 sections, 2 equations, 21 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Related Surveys
  3. LLM Fundamentals
  4. Model Architecture
  5. Encoder-only architecture
  6. Encoder-decoder architecture
  7. Decoder-only architecture
  8. LLM Pre-training
  9. Dataset collection and preprocessing
  10. Model training
  11. LLM Fine-tuning
  12. Instruction tuning
  13. Alignment tuning
  14. LLM Inference and Utilization by Prompting
  15. In-context learning (ICL)
  16. ...and 68 more sections

Figures (21)

  • Figure 1: Organization and key topics covered in this work.
  • Figure 2: Organization and key topics of Section \ref{['sec-fundamental']}.
  • Figure 3: Comparison of various prompt engineering techniques zhao2023surveymadaan2024self. Different from task-specific examples in ICL demonstrations, CoT prompting additionally incorporates intermediate reasoning steps in demonstrations. Prompt-based planning breaks down intricate tasks into manageable sub-tasks and outlines action sequences for their resolution. Self-refine enhances LLM's outputs through iterative feedback and refinement.
  • Figure 4: Illustration of different LLM deployment strategies.
  • Figure 5: Using language models for automated troubleshooting in telecom fieldsbosch2022integrating.
  • ...and 16 more figures