基于大型语言模型的代理人的终身学习:路线图
IEEE transactions on pattern analysis and machine intelligence
|January 6, 2026
概括
终身学习使人工通用智能 (AGI) 代理人能够持续适应. 本调查详细介绍了将终身学习整合到大型语言模型 (LLM) 代理人的技术,以克服静态系统的限制.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 终身学习对于推动人工通用智能 (AGI) 的发展至关重要.
- 当前的大型语言模型 (LLM) 代理通常是静态的,不能适应动态环境.
- 需要有系统的方法来使LLM代理人能够不断学习.
研究的目的:
- 提供首次对将终身学习纳入基于LLM的代理人的技术进行系统调查.
- 为了分类终身学习LLM代理的核心组成部分.
- 为开发适应性LLM代理提供路线图.
主要方法:
- 将LLM代理组件分类为感知,记忆和行动模块.
- 对现有的关于终身学习技术的文学进行系统审查.
- 分析这些组件如何实现持续适应并减轻灾难性遗忘.
主要成果:
- 在LLM中确定终身学习的关键模块 (感知,记忆,行动).
- 突出技术,使持续适应和打击灾难性遗忘.
- 总结新兴趋势,评估指标和终身学习LLM代理人的应用.
结论:
- 终身学习对于开发具有适应性和强大的LLM代理人至关重要.
- 一种模块化方法 (感知,记忆,行动) 促进了持续的学习.
- 这项调查是未来对终身学习LLM代理人研究的基础资源.
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