探索大型语言模型在自适应系统中的潜力
Exploring the Potential of Large Language Models in Self-adaptive Systems
AI总结：
本文通过跨学科文献综述，系统梳理并分类了大型语言模型在自适应系统中的潜在应用方向，为后续研究提供指引。
摘要：
大型语言模型（LLMs）凭借其在知识获取和推理方面的能力，有潜力增强自适应系统（SAS）的各个方面。然而，由于缺乏来自该领域旗舰会议或期刊（如SEAMS和TAAS）的文献，LLMs在SAS中的潜力在很大程度上仍未被探索且尚不明确。SAS的跨学科特性表明，借鉴并整合来自软件工程和自主智能体等相关领域的思想，可能为LLMs在SAS中的应用揭示创新性研究方向。为此，本文报告了对相关领域研究进行文献综述的结果，对与SAS相关的研究进行了总结和分类，并概述了它们在SAS特定方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
