Continual Learning of Large Language Models: A Comprehensive Survey
arxiv(2024)
摘要
The recent success of large language models (LLMs) trained on static,
pre-collected, general datasets has sparked numerous research directions and
applications. One such direction addresses the non-trivial challenge of
integrating pre-trained LLMs into dynamic data distributions, task structures,
and user preferences. Pre-trained LLMs, when tailored for specific needs, often
experience significant performance degradation in previous knowledge domains –
a phenomenon known as "catastrophic forgetting". While extensively studied in
the continual learning (CL) community, it presents new manifestations in the
realm of LLMs. In this survey, we provide a comprehensive overview of the
current research progress on LLMs within the context of CL. This survey is
structured into four main sections: we first describe an overview of
continually learning LLMs, consisting of two directions of continuity: vertical
continuity (or vertical continual learning), i.e., continual adaptation from
general to specific capabilities, and horizontal continuity (or horizontal
continual learning), i.e., continual adaptation across time and domains
(Section 3). We then summarize three stages of learning LLMs in the context of
modern CL: Continual Pre-Training (CPT), Domain-Adaptive Pre-training (DAP),
and Continual Fine-Tuning (CFT) (Section 4). Then we provide an overview of
evaluation protocols for continual learning with LLMs, along with the current
available data sources (Section 5). Finally, we discuss intriguing questions
pertaining to continual learning for LLMs (Section 6). The full list of papers
examined in this survey is available at
https://github.com/Wang-ML-Lab/llm-continual-learning-survey.
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