  As the applications of large language models (LLMs) expand across diverse
fields, the ability of these models to adapt to ongoing changes in data, tasks,
and user preferences becomes crucial. Traditional training methods, relying on
static datasets, are increasingly inadequate for coping with the dynamic nature
of real-world information. Lifelong learning, also known as continual or
incremental learning, addresses this challenge by enabling LLMs to learn
continuously and adaptively over their operational lifetime, integrating new
knowledge while retaining previously learned information and preventing
catastrophic forgetting. This survey delves into the sophisticated landscape of
lifelong learning, categorizing strategies into two primary groups: Internal
Knowledge and External Knowledge. Internal Knowledge includes continual
pretraining and continual finetuning, each enhancing the adaptability of LLMs
in various scenarios. External Knowledge encompasses retrieval-based and
tool-based lifelong learning, leveraging external data sources and
computational tools to extend the model's capabilities without modifying core
parameters. The key contributions of our survey are: (1) Introducing a novel
taxonomy categorizing the extensive literature of lifelong learning into 12
scenarios; (2) Identifying common techniques across all lifelong learning
scenarios and classifying existing literature into various technique groups
within each scenario; (3) Highlighting emerging techniques such as model
expansion and data selection, which were less explored in the pre-LLM era.
Through a detailed examination of these groups and their respective categories,
this survey aims to enhance the adaptability, reliability, and overall
performance of LLMs in real-world applications.
