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【第143期】构建能够终身学习的大型语言模型（LLM）代理
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Lifelong Learning of Large Language Model based Agents: A RoadmapSummary
This paper surveys techniques for building large language model (LLM) agents capable of lifelong learning. It categorizes key agent components into perception, memory, and action modules, emphasizing how these modules enable continuous adaptation and mitigate catastrophic forgetting. The authors explore various strategies for each module, including multimodal perception, diverse memory types (working, episodic, semantic, parametric), and grounding, retrieval, and reasoning actions. The paper also reviews relevant evaluation metrics and discusses real-world applications. Finally, it provides insights into future research directions, focusing on improving the integration and scalability of these modules for more robust and human-like learning.
这篇论文综述了构建能够终身学习的大型语言模型（LLM）代理的方法。论文将关键的代理组件分为感知、记忆和行动模块，强调这些模块如何促进持续适应并减轻灾难性遗忘。作者探讨了每个模块的各种策略，包括多模态感知、多样化的记忆类型（工作记忆、情节记忆、语义记忆、参数化记忆）以及基础、检索和推理行动。论文还回顾了相关的评估指标，并讨论了这些技术在现实世界中的应用。最后，作者提供了对未来研究方向的见解，重点是改进这些模块的集成性和可扩展性，以实现更强大和更像人类的学习能力。
原文链接：https://arxiv.org/abs/2501.07278
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Lifelong Learning of Large Language Model based Agents: A RoadmapSummary
This paper surveys techniques for building large language model (LLM) agents capable of lifelong learning. It categorizes key agent components into perception, memory, and action modules, emphasizing how these modules enable continuous adaptation and mitigate catastrophic forgetting. The authors explore various strategies for each module, including multimodal perception, diverse memory types (working, episodic, semantic, parametric), and grounding, retrieval, and reasoning actions. The paper also reviews relevant evaluation metrics and discusses real-world applications. Finally, it provides insights into future research directions, focusing on improving the integration and scalability of these modules for more robust and human-like learning.
这篇论文综述了构建能够终身学习的大型语言模型（LLM）代理的方法。论文将关键的代理组件分为感知、记忆和行动模块，强调这些模块如何促进持续适应并减轻灾难性遗忘。作者探讨了每个模块的各种策略，包括多模态感知、多样化的记忆类型（工作记忆、情节记忆、语义记忆、参数化记忆）以及基础、检索和推理行动。论文还回顾了相关的评估指标，并讨论了这些技术在现实世界中的应用。最后，作者提供了对未来研究方向的见解，重点是改进这些模块的集成性和可扩展性，以实现更强大和更像人类的学习能力。
原文链接：https://arxiv.org/abs/2501.07278