  In recent years, machine learning (ML) techniques have created numerous
opportunities for intelligent mobile networks and have accelerated the
automation of network operations. However, complex network tasks may involve
variables and considerations even beyond the capacity of traditional ML
algorithms. On the other hand, large language models (LLMs) have recently
emerged, demonstrating near-human-level performance in cognitive tasks across
various fields. However, they remain prone to hallucinations and often lack
common sense in basic tasks. Therefore, they are regarded as assistive tools
for humans. In this work, we propose the concept of "generative AI-in-the-loop"
and utilize the semantic understanding, context awareness, and reasoning
abilities of LLMs to assist humans in handling complex or unforeseen situations
in mobile communication networks. We believe that combining LLMs and ML models
allows both to leverage their respective capabilities and achieve better
results than either model alone. To support this idea, we begin by analyzing
the capabilities of LLMs and compare them with traditional ML algorithms. We
then explore potential LLM-based applications in line with the requirements of
next-generation networks. We further examine the integration of ML and LLMs,
discussing how they can be used together in mobile networks. Unlike existing
studies, our research emphasizes the fusion of LLMs with traditional ML-driven
next-generation networks and serves as a comprehensive refinement of existing
surveys. Finally, we provide a case study to enhance ML-based network intrusion
detection with synthesized data generated by LLMs. Our case study further
demonstrates the advantages of our proposed idea.
