Trends and Challenges of Real-time Learning in Large Language Models: A Critical Review

arXiv:2404.18311v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenges concerns cs.ai cs.lg data enabling intelligent knowledge language language models large language large language models learning systems novel real-time real-time learning review systems tasks trends type world