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：实时学习涉及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，这是一种关键能力，尤其是在数据可能不足或难以获取的情况下。本评述对大型语言模型中的实时学习进行了全面分析，综合了包括持续学习、元学习、参数高效学习以及混合专家学习在内的最新实时学习范式。通过描述这些相关主题的具体成果及其关键因素，我们展示了它们在实时学习中的效用。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，本评述为实时学习及其对基于LLM、解决现实问题的学习系统的设计与开发所蕴含的意义，提供了全面的理解。