迈向大语言模型的终身学习：综述
Towards Lifelong Learning of Large Language Models: A Survey
AI总结：
本文综述了大语言模型终身学习的研究，提出将方法分为内部知识与外部知识两大类，并建立覆盖12种场景的新分类法，系统梳理持续预训练、持续微调、检索与工具等策略，以提升模型在动态环境中的适应性与可靠性。
摘要：
随着大语言模型（LLMs）在多个领域的应用不断扩展，这些模型适应数据、任务和用户偏好持续变化的能力变得至关重要。依赖静态数据集的传统训练方法越来越难以应对现实世界信息的动态特性。终身学习，也称为持续学习或增量学习，通过使LLMs在其运行生命周期内持续、自适应地学习来应对这一挑战，在整合新知识的同时保留先前学到的信息并防止灾难性遗忘。本综述深入探讨终身学习的复杂研究格局，将策略划分为两大类：内部知识与外部知识。内部知识包括持续预训练和持续微调，各自在不同场景下增强LLMs的适应性。外部知识涵盖基于检索和基于工具的终身学习，利用外部数据源和计算工具扩展模型能力，而无需修改核心参数。本综述的主要贡献包括：（1）提出一种新的分类法，将广泛的终身学习文献划分为12种场景；（2）识别所有终身学习场景中的通用技术，并将现有文献归入每种场景下的不同技术组；（3）重点介绍模型扩展和数据选择等在LLM时代之前较少被探索的新兴技术。通过对这些技术组及其相应类别的详细考察，本综述旨在提升LLMs在现实应用中的适应性、可靠性和整体性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
