  Large Language Models (LLMs), with their abilities in knowledge acquisition
and reasoning, can potentially enhance the various aspects of Self-adaptive
Systems (SAS). Yet, the potential of LLMs in SAS remains largely unexplored and
ambiguous, due to the lack of literature from flagship conferences or journals
in the field, such as SEAMS and TAAS. The interdisciplinary nature of SAS
suggests that drawing and integrating ideas from related fields, such as
software engineering and autonomous agents, could unveil innovative research
directions for LLMs within SAS. To this end, this paper reports the results of
a literature review of studies in relevant fields, summarizes and classifies
the studies relevant to SAS, and outlines their potential to specific aspects
of SAS.
