标题： 基于大语言模型的智能体的主流范式综述：工具使用（包括RAG）、规划和反馈学习 显示英文标题

标题： A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning

摘要： 工具使用、规划和反馈学习是当前在各种任务中开发基于大型语言模型（LLM）代理的三种突出范式。 尽管为每种范式都设计了许多框架，但它们复杂的流程和不一致的分类给跨不同范式的框架理解和回顾带来了挑战。 本综述介绍了一个统一的分类法，以系统地回顾和讨论这些框架。 具体而言，1）该分类法定义了环境/任务、常见的LLM配置角色或LMPRs（策略模型、评估者和动态模型），以及先前工作中发现的通用流程，2）它能够比较不同代理范式和框架中LMPRs实现和流程设计的关键观点。 3）最后，我们识别了现有流程设计中的三个局限性，并系统地讨论了未来的工作。 资源已公开在我们的GitHub仓库https://github.com/xinzhel/LLM-Agent-Survey中。 显示英文摘要

摘要： Tool use, planning, and feedback learning are currently three prominent paradigms for developing Large Language Model (LLM)-based agents across various tasks. Although numerous frameworks have been devised for each paradigm, their intricate workflows and inconsistent taxonomy create challenges in understanding and reviewing the frameworks across different paradigms. This survey introduces a unified taxonomy to systematically review and discuss these frameworks. Specifically, 1) the taxonomy defines environments/tasks, common LLM-profiled roles or LMPRs (policy models, evaluators, and dynamic models), and universally applicable workflows found in prior work, and 2) it enables a comparison of key perspectives on the implementations of LMPRs and workflow designs across different agent paradigms and frameworks. 3) Finally, we identify three limitations in existing workflow designs and systematically discuss the future work. Resources have been made publicly available at in our GitHub repository https://github.com/xinzhel/LLM-Agent-Survey.