  Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology
across diverse sectors, offering adaptable solutions to complex challenges in
both military and civilian domains. Their expanding capabilities present a
platform for further advancement by integrating cutting-edge computational
tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms.
These advancements have significantly impacted various facets of human life,
fostering an era of unparalleled efficiency and convenience. Large Language
Models (LLMs), a key component of AI, exhibit remarkable learning and
adaptation capabilities within deployed environments, demonstrating an evolving
form of intelligence with the potential to approach human-level proficiency.
This work explores the significant potential of integrating UAVs and LLMs to
propel the development of autonomous systems. We comprehensively review LLM
architectures, evaluating their suitability for UAV integration. Additionally,
we summarize the state-of-the-art LLM-based UAV architectures and identify
novel opportunities for LLM embedding within UAV frameworks. Notably, we focus
on leveraging LLMs to refine data analysis and decision-making processes,
specifically for enhanced spectral sensing and sharing in UAV applications.
Furthermore, we investigate how LLM integration expands the scope of existing
UAV applications, enabling autonomous data processing, improved
decision-making, and faster response times in emergency scenarios like disaster
response and network restoration. Finally, we highlight crucial areas for
future research that are critical for facilitating the effective integration of
LLMs and UAVs.
