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zjunlp/LLMAgentPapers

Must-read Papers on LLM Agents.

agentagentsawsome-listenvironmentin-context-learninginstruction-followinginteractivelarge-language-modelsllmmultiagent-systemsnatural-language-processingnlppaper-listpromptreviewsurveysurveys
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创建于 2023/5/19更新于 今天
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由 Gemini 翻译整理

LLM Agent 论文列表

Awesome License: MIT

关于大语言模型(LLM)Agent 的必读论文。


"以下是你可能感兴趣的其他论文列表:

💡 Prompt4ReasoningPapers: 基于提示工程进行推理的 LLM 论文。

🔬 KnowledgeEditingPapers: 大语言模型知识编辑必读论文。

我们诚挚地邀请您深入探索这些论文集与资源,每一份列表都将为您带来独特的探索发现之旅。 :partying_face:”

🔔 最新动态

  • [2024-03] 发布新论文: "KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents"。
  • [2023-06] 创建本项目,旨在维护关于 Multi-agents 的论文列表。

📜 目录

  • LLM Agent 论文列表
    • 🔔 最新动态
    • 📜 目录
    • 🌄 论文
      • 综述
      • 🤖 Agent
        • 人格 (Personality) 🧛🧙
        • 记忆 (Memory) 💭💫
        • 规划 (Planning) 🧩♟️
        • 工具使用 (Tool use) 👩‍🔧🔧
        • 强化学习训练 (RL training) 🧠📈
      • 🤖💬🤖 多智能体系统 (Multiple Agents)
        • 任务导向型通信
          • 协作交流 (Collaborative Exchanges) 👨‍💻👩‍💻
          • 对抗交互 (Adversarial Interactions) 👨🏻‍🦳🗣
        • 闲聊/开放式对话 (Casual/Open Conversations) 👥💬
      • 🪐 应用 (Application)
      • 🖼️ 框架 (Framework)
      • 🔖 其他 (Others)
    • 🧰 资源 (Resources)
      • 基准测试 (Benchmarks)
      • 工具类型 (Types of Tools)
      • 📜 工具列表 (Tool List)
    • 🎉 贡献
      • 如何贡献
      • 贡献者名单

🌄 论文

综述

  1. Interactive Natural Language Processing

    Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang, Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu, Yike Guo, Jie Fu. [abs], 2023.5

  2. A Survey on Large Language Model based Autonomous Agents

    Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen. [abs], 2023.8

  3. The Rise and Potential of Large Language Model Based Agents: A Survey

    Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang, Wenjuan Qin, Yongyan Zheng, Xipeng Qiu, Xuanjing Huang, Tao Gui. [abs], 2023.9

  4. If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents

    Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R. Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, Heng Ji, Chengxiang Zhai. [abs], 2024.1

  5. Agent AI: Surveying the Horizons of Multimodal Interaction

    Zane Durante, Qiuyuan Huang, Naoki Wake, Ran Gong, Jae Sung Park, Bidipta Sarkar, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Yejin Choi, Katsushi Ikeuchi, Hoi Vo, Li Fei-Fei, Jianfeng Gao. [abs], 2024.1

  6. Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

    Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, Rui Kong, Yile Wang, Hanfei Geng, Jian Luan, Xuefeng Jin, Zilong Ye, Guanjing Xiong, Fan Zhang, Xiang Li, Mengwei Xu, Zhijun Li, Peng Li, Yang Liu, Ya-Qin Zhang, Yunxin Liu. [abs], 2024.1

  7. A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond

    Qiushi Sun, Zhirui Chen, Fangzhi Xu, Kanzhi Cheng, Chang Ma, Zhangyue Yin, Jianing Wang, Chengcheng Han, Renyu Zhu, Shuai Yuan, Qipeng Guo, Xipeng Qiu, Pengcheng Yin, Xiaoli Li, Fei Yuan, Lingpeng Kong, Xiang Li, Zhiyong Wu. [abs], 2024.3

  8. A Survey on Large Language Model based Human-Agent Systems

    Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Yankai Chen, Chunyu Miao, Hoang Ng

贡献者
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项目信息
默认分支main
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创建时间2023/5/19
最近更新今天
GAI 中文摘要

LLMAgentPapers 是一个专门收集大语言模型智能体领域核心论文的开源项目,旨在为研究人员和开发者提供全面、结构化的学术资源导航。该项目汇集了智能体技术的前沿研究,帮助用户快速了解这一领域的演进脉络与技术现状。

核心功能包括:对大模型智能体的基础能力如记忆、规划、工具使用及强化学习训练进行分类整理,涵盖了多智能体协作、对抗交互及开放对话的研究成果,系统性地收录了各类智能体应用场景与开发框架,并提供了针对性的基准测试工具与学术资源支持。

本项目适用于大语言模型领域的研究者、AI 开发人员以及对自主智能体技术感兴趣的技术从业者,是深入探索智能体架构、多智能体系统及相关前沿算法的必备参考资料。