Continual Learning for Large Language Models: A Survey
CoRR(2024)
摘要
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.
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