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.

翻译：大语言模型凭借其知识获取与推理能力，有望增强自适应性系统的多个方面。然而，由于该领域顶级会议或期刊（如SEAMS、TAAS）相关文献的缺乏，大语言模型在自适应性系统中的潜力仍未得到充分探索，且存在模糊性。自适应性系统的跨学科特性表明，从软件工程、自主智能体等相关领域汲取并整合思想，可能为自适应性系统内的大语言模型开辟创新研究方向。为此，本文报告了相关领域文献综述的研究结果，对与自适应性系统相关的研究进行总结与分类，并概述其对自适应性系统特定方面的潜在价值。