Large language models (LLMs) are not amenable to frequent re-training, due to
high training costs arising from their massive scale. However, updates are
necessary to endow LLMs with new skills and keep them up-to-date with rapidly
evolving human knowledge. This paper surveys recent works on continual learning
for LLMs. Due to the unique nature of LLMs, we catalog continue learning
techniques in a novel multi-staged categorization scheme, involving continual
pretraining, instruction tuning, and alignment. We contrast continual learning
for LLMs with simpler adaptation methods used in smaller models, as well as
with other enhancement strategies like retrieval-augmented generation and model
editing. Moreover, informed by a discussion of benchmarks and evaluation, we
identify several challenges and future work directions for this crucial task.

该论文调研了关于大型语言模型（LLMs）的持续学习方面的最新工作，提出了一种新颖的多阶段分类方案，涉及持续预训练、指令调整和对齐；对 LLMs 的持续学习与更简单的小模型的适应方法以及其他增强策略进行对比；同时，在讨论基准和评估的基础上，提出了几个挑战和未来工作方向。