Real-time learning concerns the ability of learning systems to acquire knowledge over time, enabling their adaptation and generalization to novel tasks. It is a critical ability for intelligent, real-world systems, especially when data may be insufficient or difficult to obtain. This review provides a comprehensive analysis of real-time learning in Large Language Models. It synthesizes the state-of-the-art real-time learning paradigms, including continual learning, meta-learning, parameter-efficient learning, and mixture-of-experts learning. We demonstrate their utility for real-time learning by describing specific achievements from these related topics and their critical factors. Finally, the paper highlights current problems and challenges for future research in the field. By consolidating the latest relevant research developments, this review offers a comprehensive understanding of real-time learning and its implications for designing and developing LLM-based learning systems addressing real-world problems.

翻译：实时学习关注学习系统随时间获取知识的能力，使其能够适应并泛化至新任务。对于智能化的现实世界系统而言，这一能力至关重要，尤其当数据可能不充足或难以获取时。本综述对大型语言模型中的实时学习进行了全面分析。它综合了最先进的实时学习范式，包括持续学习、元学习、参数高效学习以及混合专家学习。我们通过描述这些相关领域的具体成果及其关键因素，展示了它们在实时学习中的实用性。最后，本文指出了当前存在的问题及未来研究面临的挑战。通过整合最新的相关研究进展，本综述为理解实时学习及其对设计开发基于LLM的、解决现实问题的学习系统所蕴含的意义，提供了全面的认识。