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Recent Advances of Foundation Language Models-based Continual Learning: A Survey (2405.18653v2)

Published 28 May 2024 in cs.CL

Abstract: Recently, foundation LLMs (LMs) have marked significant achievements in the domains of NLP and computer vision (CV). Unlike traditional neural network models, foundation LMs obtain a great ability for transfer learning by acquiring rich commonsense knowledge through pre-training on extensive unsupervised datasets with a vast number of parameters. However, they still can not emulate human-like continuous learning due to catastrophic forgetting. Consequently, various continual learning (CL)-based methodologies have been developed to refine LMs, enabling them to adapt to new tasks without forgetting previous knowledge. However, a systematic taxonomy of existing approaches and a comparison of their performance are still lacking, which is the gap that our survey aims to fill. We delve into a comprehensive review, summarization, and classification of the existing literature on CL-based approaches applied to foundation LLMs, such as pre-trained LLMs (PLMs), LLMs and vision-LLMs (VLMs). We divide these studies into offline CL and online CL, which consist of traditional methods, parameter-efficient-based methods, instruction tuning-based methods and continual pre-training methods. Offline CL encompasses domain-incremental learning, task-incremental learning, and class-incremental learning, while online CL is subdivided into hard task boundary and blurry task boundary settings. Additionally, we outline the typical datasets and metrics employed in CL research and provide a detailed analysis of the challenges and future work for LMs-based continual learning.

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Authors (8)
  1. Yutao Yang (3 papers)
  2. Jie Zhou (687 papers)
  3. Xuanwen Ding (4 papers)
  4. Tianyu Huai (4 papers)
  5. Shunyu Liu (47 papers)
  6. Qin Chen (57 papers)
  7. Liang He (202 papers)
  8. Yuan Xie (188 papers)
Citations (6)
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