🐨CoALA: 出色的语言代理
**认知架构语言代理(🐨CoALA)**框架的语言代理合集。
- CoALA 论文(主体内容共16页):https://arxiv.org/abs/2309.02427
- CoALA 推特帖子(6条推文):https://twitter.com/ShunyuYao12/status/1699396834983362690
- CoALA BibTex 文件,包含300多个相关引用:CoALA.bib
- 如果你觉得我们的工作/资源有用,可以使用以下CoALA BibTex引用:
@misc{sumers2023cognitive,
title={Cognitive Architectures for Language Agents},
author={Theodore Sumers and Shunyu Yao and Karthik Narasimhan and Thomas L. Griffiths},
year={2023},
eprint={2309.02427},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
🐨CoALA 概述
CoALA 清晰地定义了语言代理的动作空间,其包括两个部分:
- 与外部环境交互的外部动作(接地)
- 与内部记忆交互的内部动作(推理、检索、学习)
- 语言代理有短期工作记忆和几个(可选的)长期记忆(用于经验的情景记忆、用于知识的语义记忆、用于代码/LLM的程序记忆)
- 推理 = 更新工作记忆(使用LLM)
- 检索 = 读取长期记忆
- 学习 = 写入长期记忆
那么语言代理如何选择采取哪种动作呢?它的动作结构化为决策循环,每个循环有两个阶段:
- 规划:代理应用推理/检索动作(迭代地)提议并评估动作,然后选择一个学习/接地动作。
- 执行:执行选择的学习/接地动作以影响内部记忆或外部世界。
想了解更多内容,请阅读我们论文的第4节。
论文
以下仅为从 CoALA.bib 抓取的论文子集加上拉取请求,可能标注有错误的动作空间标签。日期基于arxiv v1。它们不代表所有语言代理的工作,我们计划很快添加更多工作(欢迎拉取请求),并为高引用工作添加标签。
- (2021-10) AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (reasoning)
- (2021-10) SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark (environment)
- (2022-01) Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents (grounding)
- (2022-03) PromptChainer: Chaining Large Language Model Prompts through Visual Programming (grounding)
- (2022-03) ScienceWorld: Is your Agent Smarter than a 5th Grader? (environment)
- (2022-04) Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (grounding)
- (2022-04) Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language (grounding)
- (2022-07) WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (environment)
- (2022-09) ProgPrompt: Generating Situated Robot Task Plans using Large Language Models (grounding)
- (2022-10) Decomposed Prompting: A Modular Approach for Solving Complex Tasks (reasoning)
- (2022-10) Mind's Eye: Grounded Language Model Reasoning through Simulation (grounding)
- (2022-10) ReAct: Synergizing Reasoning and Acting in Language Models (grounding, reasoning)
- (2022-11) Large Language Models Are Human-Level Prompt Engineers (reasoning)
- (2022-12) LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models (grounding)
- (2022-12) Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments (grounding)
- (2023-02) Chain of Hindsight Aligns Language Models with Feedback (learning)
- (2023-02) Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents (grounding, reasoning)
- (2023-02) Toolformer: Language Models Can Teach Themselves to Use Tools (grounding)
- (2023-03) Foundation Models for Decision Making: Problems, Methods, and Opportunities (survey)
- (2023-03) HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face (grounding)
- (2023-03) PaLM-E: An Embodied Multimodal Language Model (grounding)
- (2023-03) Reflexion: Language Agents with Verbal Reinforcement Learning (grounding, reasoning, learning)
- (2023-03) Self-Refine: Iterative Refinement with Self-Feedback (reasoning)
- (2023-03) Self-planning Code Generation with Large Language Models (reasoning)
- (2023-04) Generative Agents: Interactive Simulacra of Human Behavior (grounding, reasoning, retrieval, learning)
- (2023-04) Emergent autonomous scientific research capabilities of large language models (grounding, reasoning)
- (2023-04) LLM+P: Empowering Large Language Models with Optimal Planning Proficiency (grounding, reasoning)
- (2023-04) REFINER: Reasoning Feedback on Intermediate Representations (reasoning)
- (2023-04) Teaching Large Language Models to Self-Debug (reasoning)
- (2023-04) GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information (grounding, reasoning)
- (2023-05) CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing (grounding, reasoning, retrieval)
- (2023-05) Augmenting Autotelic Agents with Large Language Models (grounding, reasoning, retrieval, learning)
- (2023-05) ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (grounding, reasoning)
- (2023-05) ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings (grounding, reasoning)
- (2023-05) Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding (reasoning)
- (2023-05) Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate (grounding, reasoning)
- (2023-05) Improving Factuality and Reasoning in Language Models through Multiagent Debate (grounding, reasoning)
- (2023-05) AdaPlanner: Adaptive Planning from Feedback with Language Models (grounding, retrieval, learning)
- (2023-05) Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (reasoning)
- (2023-05) ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models (grounding, reasoning)
- (2023-05) SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks (grounding, reasoning)
- (2023-05) Tree of Thoughts: Deliberate Problem Solving with Large Language Models (reasoning)
- (2023-05) Voyager: An Open-Ended Embodied Agent with Large Language Models (grounding, reasoning, retrieval, learning)
- (2023-06) InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback (grounding, reasoning)
- (2023-06) ToolQA: A Dataset for LLM Question Answering with External Tools (grounding)
- (2023-06) Mind2Web: Towards a Generalist Agent for the Web (environment)
- (2023-06) RestGPT: Connecting Large Language Models with Real-World RESTful APIs (grounding, reasoning)
- (2023-06) ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases (grounding, reasoning)
- (2023-07) A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis (grounding, reasoning)
- (2023-07) RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (grounding)
- (2023-07) RoCo: Dialectic Multi-Robot Collaboration with Large Language Models (grounding)
- (2023-07) Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners (grounding)
- (2023-07) S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents (grounding, reasoning)
- (2023-07) ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs (grounding, reasoning, retrieval)
- (2023-07) Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support (grounding)
- (2023-07) Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration (grounding, reasoning)
- (2023-07) WebArena: A Realistic Web Environment for Building Autonomous Agents (environment)
- (2023-08) AgentBench: Evaluating LLMs as Agents (environment)
- (2023-08) AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents (environment)
- (2023-08) AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework (grounding, reasoning)
- (2023-08) CGMI: Configurable General Multi-Agent Interaction Framework (grounding, reasoning)
- (2023-08) ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate (grounding, reasoning)
- (2023-08) Cumulative Reasoning with Large Language Models (reasoning)
- (2023-08) ExpeL: LLM Agents Are Experiential Learners (基础, 推理, 检索, 学习)
- (2023-08) GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems (基础, 推理)
- (2023-08) Gentopia: A Collaborative Platform for Tool-Augmented LLMs (环境)
- (2023-08) MetaGPT: Meta Programming for Multi-Agent Collaborative Framework (基础, 推理)
- (2023-08) ProAgent: Building Proactive Cooperative AI with Large Language Models (基础, 推理)
- (2023-08) Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization (基础, 推理, 学习)
- (2023-08) SAPIEN: Affective Virtual Agents Powered by Large Language Models (基础, 推理)
- (2023-08) Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis (基础, 推理, 检索, 学习)
- (2023-09) ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving (基础, 推理, 学习)
- (2023-09) Identifying the Risks of LM Agents with an LM-Emulated Sandbox (环境)
- (2023-09) Suspicion Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4 (基础, 推理)
- (2024-03) LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning. (规划, 推理)
- (2024-04) Empowering Biomedical Discovery with AI Agents (AI 科学家, 生物医学研究)
- (2024-05) TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models (推理, 检索) (更多内容即将添加。欢迎提交拉取请求。)
资源
(更多内容即将添加。欢迎提交拉取请求。)