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LLMエージェントの有名な論文やOSSについての調査

🗓 Created on 4/4/2024

📜 要約

主題と目的の要約

今回の調査では、「LLMエージェントの有名な論文やOSSとアーキテクチャについて」に焦点を当てました。主題と目的は、LLMエージェントに関連する重要な論文やオープンソースソフトウェア（OSS）のアーキテクチャについて、包括的な調査を行い、その重要性や応用について客観的に分析することです。

主要な内容と発見

調査の中で主要な内容と重要な発見は以下の通りです：

GPT-4や他のLLMに関する論文の初期実験やAIの危険性、影響、新技術の応用に関する研究が紹介されている。
LLMエージェントのアーキテクチャには、計画モジュール、メモリモジュール、ツールの統合が含まれており、機能の拡張が図られている。
オープンソースツールやアプリケーションの役割は、LLMやLLMエージェントを使用してタスクやコミュニケーションを改善することを目指している。

結果と結論のまとめ

得られた主要な結果と結論は以下の通りです：

RAISEやAIOSなどのアーキテクチャは、LLMエージェントの性能や効率性を向上させることが示されており、AI分野に貢献している。
LLMアプリケーションのセキュリティとプライバシーに関する考慮事項は、OWASPの脆弱性やプライバシーリスクに焦点を当てている。
AIエージェントは、自律的な意思決定や高度な自動化レベルへの移行を予示し、ワークフロー全体の再設計や自動化を可能にする可能性がある。

🔍 詳細

🏷導入: LLMエージェントとは

LLMエージェントの概要

LLMエージェントは、大規模な言語モデル（LLM）を使用して会話を続けたり、タスクを実行したりするAIシステムであり、プロンプトやメモリを活用して機能します。エージェントの構成要素は、LLM、プロンプト、メモリであり、知識の統合やツールの統合により機能を拡張します。

LLMエージェントの開発と研究

LLMエージェントの開発には、データエンジニアリング、データウェアハウスの構築、LLM-Agentの開発ステージ、最終調整の段階が含まれます。プロンプトやメモリの活用、知識の統合、ツールの統合は、エージェントの能力を向上させる重要な要素です。さらに、LLMエージェントの研究論文やOSSの活用も重要であり、最新の技術動向に対応するために積極的な情報収集が必要です。

LLMエージェントの事例と研究論文

Interactive Natural Language Processing
- 2023年5月に発表された論文
- 著者: Zekun Wang, Ge Zhang, Kexin Yang 他
- 論文リンク
大規模言語モデルに基づく自律エージェントに関する調査
- 2023年8月に発表された論文
- 著者: Lei Wang, Chen Ma, Xueyang Feng 他
- 論文リンク
大規模言語モデルベースのエージェントの台頭と潜在能力に関する調査
- 2023年9月に発表された論文
- 著者: Zhiheng Xi, Wenxiang Chen, Xin Guo 他
- 論文リンク
LLMが魔法使いなら、コードは杖: コードが大規模言語モデルを知的エージェントとして活用する方法に関する調査
- 2024年1月に発表された論文
- 著者: Ke Yang, Jiateng Liu, John Wu 他
- 論文リンク
エージェントAI: マルチモーダルインタラクションの地平を調査
- 2024年1月に発表された論文
- 著者: Zane Durante, Qiuyuan Huang, Naoki Wake 他
- 論文リンク

ionio.ai

Radford et al. (2019)

medium.com

Miro

https://youtube.com/shorts/0DJqX_Ihwoo?feature=share

nvidia.com

large language model

Retrieval-Augmented Generation (RAG)

Building Your First Agent Application

LLM-Powered Agents: Building Your First Agent Application

Generative Agents

ChatDev

recommendation systems

Building Your First Agent Application

Build an LLM-Powered API Agent for Task Execution

Build an LLM-Powered Data Agent for Data Analysis

github.com

2024-03]**We release a new paper: " [KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents

2023-06]**We create this repository to maintain a paper list on *Multi-agents*. [LLM Agents Papers

https://github.com/zjunlp/LLMAgentPapers/graphs/contributors

🏷GPTや他のLLMに関する論文の重要性

GPTや他のLLMに関する論文の重要性の要約

「GPTや他のLLMに関する論文の重要性」というセクションでは、GPT-4の初期実験やAIの危険性、影響、新しい技術の応用、AIの限界に関する研究が紹介されています。これらの論文は、GPT/LLMの機能、限界、および使用に関する重要な洞察を提供しています。

考察

これらの論文は、GPTや他のLLMに関する研究の多様な側面を網羅しており、AIの将来における重要な課題や可能性について深く考察しています。特に、AIの危険性や限界に関する研究は、技術の進化と社会への影響を考える上で重要な示唆を提供しています。これらの論文を通じて、AI技術の発展に伴う倫理的な問題や課題について議論を深めることができます。

大規模言語モデルベースの自律エージェントに関する調査

「大規模言語モデルベースの自律エージェントに関する調査」では、LLMsの台頭により、人間レベルの知能を持つ可能性が示され、LLMベースの自律エージェントに関する研究が急増しています。エージェントの構築、応用、評価の3つの側面に焦点を当て、LLMsをより効果的に活用するためのアーキテクチャの設計やエージェントの能力向上方法について探求しています。

github.com

Awesome-LLM-hallucination

awesome-hallucination-detection

LLMsPracticalGuide

Awesome ChatGPT Prompts

awesome-chatgpt-prompts-zh

Awesome ChatGPT

Chain-of-Thoughts Papers

Awesome Deliberative Prompting

Instruction-Tuning-Papers

LLM Reading List

Reasoning using Language Models

Awesome LLM Human Preference Datasets

RWKV-howto

ModelEditingPapers

Awesome LLM Security

Awesome-Align-LLM-Human

Awesome-Code-LLM

Awesome-LLM-Compression

Awesome-LLM-Systems

awesome-llm-webapps

awesome-japanese-llm

Awesome-LLM-Healthcare

Awesome-LLM-Inference

OpenCompass 2.0 LLM Leaderboard]- OpenCompass is an LLM evaluation platform, supporting a wide range of models (InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets. [Gemma

General Language Model (GLM)

Chat with DeepSeek (Beta)

lm-evaluation-harness

Text Generation Inference

Text-Embeddings-Inference

FLAML (A Fast Library for Automated Machine Learning & Tuning)

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

GitHub repository

Build a Large Language Model (From Scratch)

A Stage Review of Instruction Tuning

2023-06-29] [Yao Fu] [LLM Powered Autonomous Agents

2023-06-23] [Lilian] [Why you should work on AI AGENTS!

2023-06-22] [Andrej Karpathy] [Google "We Have No Moat, And Neither Does OpenAI"

2023-05-05] [AI competition statement

2023-04-20] [petergabriel] [我的大模型世界观

2023-04-23] [陆奇] [Prompt Engineering

2023-03-15] [Lilian] [Noam Chomsky: The False Promise of ChatGPT

2023-03-08][Noam Chomsky] [Is ChatGPT 175 Billion Parameters? Technical Analysis

2023-03-04][Owen] [Towards ChatGPT and Beyond

2023-02-20][知乎][欧泽彬] [追赶ChatGPT的难点与平替

2023-02-19][李rumor] [对话旷视研究院张祥雨｜ChatGPT的科研价值可能更大

2023-02-16][知乎][旷视科技] [关于ChatGPT八个技术问题的猜想

2023-02-15][知乎][张家俊] [ChatGPT发展历程、原理、技术架构详解和产业未来

2023-02-15][知乎][陈巍谈芯] [对ChatGPT的二十点看法

2023-02-13][知乎][熊德意] [ChatGPT-所见、所闻、所感

2023-02-11][知乎][刘聪NLP] [The Next Generation Of Large Language Models

2023-02-07][Forbes] [Large Language Model Training in 2023

2023-02-03][Cem Dilmegani] [What Are Large Language Models Used For?

2023-01-26][NVIDIA] [Large Language Models: A New Moore's Law

2021-10-26][Huggingface] [Arize-Phoenix

Emergent Mind

ShareGPT

Major LLMs + Data Availability

500+ Best AI Tools

Cohere Summarize Beta

arxiv.org

github.com

A Survey on Large Language Model-Based Game Agents

Advancing LLM Reasoning Generalists with Preference Trees

LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns

Gecko: Versatile Text Embeddings Distilled from Large Language Models

ITCMA: A Generative Agent Based on a Computational Consciousness Structure

Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning

MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation

Change-Agent: Towards Interactive Comprehensive Change Interpretation and Analysis from Change Detection and Change Captioning

Long-form factuality in large language models

Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior Simulation

A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks

A Study of Three Influencer Archetypes for the Control of Opinion Spread in Time-Varying Social Networks

MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution

Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games

AIOS: LLM Agent Operating System

RepairAgent: An Autonomous, LLM-Based Agent for Program Repair

CYGENT: A cybersecurity conversational agent with log summarization powered by GPT-3

TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models

When LLM-based Code Generation Meets the Software Development Process

Can large language models explore in-context?

CoLLEGe: Concept Embedding Generation for Large Language Models

LLM-Driven Agents for Influencer Selection in Digital Advertising Campaigns

Language Models in Dialogue: Conversational Maxims for Human-AI Interactions

CACA Agent: Capability Collaboration based AI Agent

ReAct Meets ActRe: Autonomous Annotations of Agent Trajectories for Contrastive Self-Training

ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification

PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning

RoleInteract: Evaluating the Social Interaction of Role-Playing Agents

Polaris: A Safety-focused LLM Constellation Architecture for Healthcare

Large Language Models meet Network Slicing Management and Orchestration

Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal

Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning

Characteristic AI Agents via Large Language Models

Embodied LLM Agents Learn to Cooperate in Organized Teams

Contextual Moral Value Alignment Through Context-Based Aggregation

Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models

EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents

From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models

https://arxiv.org/abs/2403.11835](Agent3D-Zero

DiPaCo: Distributed Path Composition

PERL: Parameter Efficient Reinforcement Learning from Human Feedback

AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automation

Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning

Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?

Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models

VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding

From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News

LLM-based agents for automating the enhancement of user story quality: An early report

USimAgent: Large Language Models for Simulating Search Users

SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents

AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents

TINA: Think, Interaction, and Action Framework for Zero-Shot Vision Language Navigation

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation

Cultural evolution in populations of Large Language Models

CleanAgent: Automating Data Standardization with LLM-based Agents

NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning

WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations

DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation

AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

Academically intelligent LLMs are not necessarily socially intelligent

TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision

ArgMed-Agents: Explainable Clinical Decision Reasoning with Large Language Models via Argumentation Schemes

Reframe Anything: LLM Agent for Open World Video Reframing

Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks

RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation

FLAP: Flow Adhering Planning with Constrained Decoding in LLMs

Acceleron: A Tool to Accelerate Research Ideation

PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion

SheetAgent: A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models

Exploring LLM-based Agents for Root Cause Analysis

Language Guided Exploration for RL Agents in Text Environments

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents

Learning to Use Tools via Cooperative and Interactive Agents

OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following

Android in the Zoo: Chain-of-Action-Thought for GUI Agents

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Entropy-Regularized Token-Level Policy Optimization for Large Language Models

ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary

Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code

Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents

Human Simulacra: A Step toward the Personification of Large Language Models

Prospect Personalized Recommendation on Large Language Model-based Agent Platform

Data Interpreter: An LLM Agent For Data Science

ByteComposer: a Human-like Melody Composition Method based on Language Model Agent

Genie: Generative Interactive Environments

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

User-LLM: Efficient LLM Contextualization with User Embeddings

∞Bench: Extending Long Context Evaluation Beyond 100K Tokens

Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent

MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion

Large Language Model-based Human-Agent Collaboration for Complex Task Solving

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM Agents

WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment

Comprehensive Cognitive LLM Agent for Smartphone GUI Automation

LLM Agents for Psychology: A Study on Gamified Assessments

Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations

LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Modelling Political Coalition Negotiations Using LLM-based Agents

LLM can Achieve Self-Regulation via Hyperparameter Aware Generation

Robust agents learn causal world models

Chain-of-Thought Reasoning Without Prompting

A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems

Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications

DoRA: Weight-Decomposed Low-Rank Adaptation

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback

Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

Simulating Human Strategic Behavior: Comparing Single and Multi-agent LLMs

Large Language Models as Minecraft Agents

PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment

OS-Copilot: Towards Generalist Computer Agents with Self-Improvement

Predictive representations: building blocks of intelligence

Secret Collusion Among Generative AI Agents

THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation

Self-Correcting Self-Consuming Loops for Generative Model Training

V-STaR: Training Verifiers for Self-Taught Reasoners

Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Feedback Loops With Language Models Drive In-Context Reward Hacking

Understanding the Weakness of Large Language Model Agents within a Complex Android Environment

Large Language Models: A Survey

Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet

An Interactive Agent Foundation Model

UFO: A UI-Focused Agent for Windows OS Interaction

Real-World Robot Applications of Foundation Models: A Review

TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation

ScreenAgent: A Vision Language Model-driven Computer Control Agent

In-Context Principle Learning from Mistakes

Discovering Temporally-Aware Reinforcement Learning Algorithms

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis

Decision Theory-Guided Deep Reinforcement Learning for Fast Learning

The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends

Can Large Language Model Agents Simulate Human Trust Behaviors?

ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Self-Discover: Large Language Models Self-Compose Reasoning Structures

AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls

Can Generative Agents Predict Emotion?

S-Agents: self-organizing agents in open-ended environment

Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning

MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model

Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models

In-context learning agents are asymmetric belief updaters

Systematic Biases in LLM Simulations of Debates

Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science

Chain-of-Feedback: Mitigating the Effects of Inconsistency in Responses

Vision-Language Models Provide Promptable Representations for Reinforcement Learning

Guiding Language Model Math Reasoning with Planning Tokens

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models

Graph-enhanced Large Language Models in Asynchronous Plan Reasoning

Understanding the planning of LLM agents: A survey

Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning

Collaborative Agents for Software Engineering

K-Level Reasoning with Large Language Models

Efficient Exploration for LLMs

Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents

StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis

Efficient Tool Use with Chain-of-Abstraction Reasoning

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios

Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation

Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception

code

Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis

Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

YODA: Teacher-Student Progressive Learning for Language Models

Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion

Hi-Core: Hierarchical Knowledge Transfer for Continual Reinforcement Learning

Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents

HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments

Memory Matters: The Need to Improve Long-Term Memory in LLM-Agents

OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics

WARM: On the Benefits of Weight Averaged Reward Models

PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety

AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology

The Conversation is the Command: Interacting with Real-World Autonomous Robot Through Natural Language

Tool-LMM: A Large Multi-Modal Model for Tool Agent Learning

A match made in consistency heaven: when large language models meet evolutionary algorithms

CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents

Self-Rewarding Language Models

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents

Large Language Models Are Neurosymbolic Reasoners

ReFT: Reasoning with Reinforced Fine-Tuning

Scalable Pre-training of Large Autoregressive Image Models

What makes for a 'good' social actor? Using respect as a lens to evaluate interactions with language agents

https://arxiv.org/abs/2401.08500](Code

MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World

DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models

Self-Imagine: Effective Unimodal Reasoning with Multimodal Models using Self-Imagination

Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening

Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Exploring the Potential of Large Language Models in Self-adaptive Systems

A Study on Training and Developing Large Language Models for Behavior Tree Generation

When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment

CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent

ModaVerse: Efficiently Transforming Modalities with LLMs

AntEval: Quantitatively Evaluating Informativeness and Expressiveness of Agent Social Interactions

Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Case Study

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction

Designing Heterogeneous LLM Agents for Financial Sentiment Analysis

Resource-cloud

Evidence to Generate (E2G): A Single-agent Two-step Prompting for Context Grounded and Retrieval Augmented Reasoning

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

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

The Impact of Reasoning Step Length on Large Language Models

InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks

Agent Alignment in Evolving Social Norms

Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects

Metacognition is all you need? Using Introspection in Generative Agents to Improve Goal-directed Behavior

Agent AI: Surveying the Horizons of Multimodal Interaction

LLaVA-ϕ: Efficient Multi-Modal Assistant with Small Language Model

Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning

Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes

Economics Arena for Large Language Models

LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

AppAgent: Multimodal Agents as Smartphone Users

Capture the Flag: Uncovering Data Insights with Large Language Models

AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines

DSPy

ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation

Generative agents in the streets: Exploring the use of Large Language Models (LLMs) in collecting urban perceptions

dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Models

Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives

Agent Assessment of Others Through the Lens of Self

Evaluating Language-Model Agents on Realistic Autonomous Tasks

LLM-ARK: Knowledge Graph Reasoning Using Large Language Models via Deep Reinforcement Learning

ProTIP: Progressive Tool Retrieval Improves Planning

ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

Practices for Governing Agentic AI Systems

TinyGSM: achieving >80% on GSM8k with small language models

Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent

Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory

LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers

LLMind: Orchestrating AI and IoT with LLMs for Complex Task Execution

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Adaptive parameter sharing for multi-agent reinforcement learning

Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft

Vision-Language Models as a Source of Rewards

Learning Coalition Structures with Games

diff History for Long-Context Language Agents

Sequential Planning in Large Partially Observable Environments guided by LLMs

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

STaR

KwaiAgents: Generalized Information-seeking Agent System with Large Language Models

Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making

Generating Illustrated Instructions

Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use

Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia

LLM as OS (llmao), Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem

https://arxiv.org/abs/2312.03052

Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Constructio

Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication

LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

Towards Learning a Generalist Model for Embodied Navigation

Universal Self-Consistency for Large Language Model Generation

STaR-method

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Some intuitions about large language models

Building the Future of Responsible AI: A Pattern-Oriented Reference Architecture for Designing Large Language Model based Agents

System 2 Attention (is something you might need too)

Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents

A Language Agent for Autonomous Driving

Digital Socrates: Evaluating LLMs through explanation critiques

AutoMix: Automatically Mixing Language Models

DeepThought: An Architecture for Autonomous Self-motivated Systems

LLM Augmented Hierarchical Agents

Prompt Engineering a Prompt Engineer

ADaPT: As-Needed Decomposition and Planning with Language Models

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

Youtube. Adam Kalai presents "Recursive Self-improving Code Generation - talk 2.11.2023

Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents

SAGE: Smart home Agent with Grounded Execution

Efficient Human-AI Coordination via Preparatory Language-based Convention

Generating Sequences by Learning to Self-Correct

Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback

Towards A Natural Language Interface for Flexible Multi-Agent Task Assignment

Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models

Multi-Agent Consensus Seeking via Large Language Models

CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents

PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models

Diverse Conventions for Human-AI Collaboration

Woodpecker: Hallucination Correction for Multimodal Large Language Models

In-Context Learning Creates Task Vectors

Instruct and Extract: Instruction Tuning for On-Demand Information Extraction

Function Vectors in Large Language Models

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

Democratizing Reasoning Ability: Tailored Learning from Large Language Model

AgentTuning: Enabling Generalized Agent Abilities for LLMs

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

The next grand challenge for AI

OpenAgents: An Open Platform for Language Agents in the Wild

Improving Large Language Model Fine-tuning for Solving Math Problems

CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

A Zero-Shot Language Agent for Computer Control with Structured Reflection

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

Octopus: Embodied Vision-Language Programmer from Environmental Feedback

MemGPT: Towards LLMs as Operating Systems

Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques

Towards Robust Multi-Modal Reasoning via Model Selection

The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models

Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting

LangNav: Language as a Perceptual Representation for Navigation

FireAct: Toward Language Agent Fine-tuning

Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading

Crystal: Introspective Reasoners Reinforced with Self-Feedback

Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Agent Instructs Large Language Models to be General Zero-Shot Reasoners

Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

Lyfe Agents: Generative agents for low-cost real-time social interactions

EcoAssistant: Using LLM Assistant More Affordably and Accurately

Large Language Models as Analogical Reasoners

Conceptual Framework for Autonomous Cognitive Entities

OceanGPT: A Large Language Model for Ocean Science Tasks

SmartPlay : A Benchmark for LLMs as Intelligent Agents

GRID: A Platform for General Robot Intelligence Development

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models

Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

You only look at the screens: Multimodal Chain-of-Action Agents

MindAgent: Emergent Gaming Interaction

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

Agents: An Open-source Framework for Autonomous Language Agents

Physically Grounded Vision-Language Models for Robotic Manipulation

Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents

Unleashing the Power of Graph Learning through LLM-based Autonomous Agents

RecMind: Large Language Model Powered Agent For Recommendation

A Survey on Large Language Model based Autonomous Agents

https://arxiv.org/abs/2308.10848

Graph of Thoughts: Solving Elaborate Problems with Large Language Models

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reinforced Self-Training (ReST) for Language Modeling

Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

WebArena: A Realistic Web Environment for Building Autonomous Agents

Communicative Agents for Software Development

ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

SELFEVOLVE: A Code Evolution Framework via Large Language Models

Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services

Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions

Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models

Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing

Voyager: An Open-Ended Embodied Agent with Large Language Models

Gorilla: Large Language Model Connected with Massive APIs

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

ImageBind: One Embedding Space To Bind Them All

Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings

BabyBeeAGI: Task Management and Functionality Expansion on top of BabyAGI

Entire Talk" by Stanford eCorner

Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

ChemCrow: Augmenting large-language models with chemistry tools

Teaching Large Language Models to Self-Debug

ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions

Generative Agents: Interactive Simulacra of Human Behavior

GPTeam

CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society

Self-Refine: Iterative Refinement with Self-Feedback

https://selfrefine.info/

HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace

DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents

TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications

Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reflexion: Language Agents with Verbal Reinforcement Learning

Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

Large Language Models Can Self-Improve

Emergent Abilities of Large Language Models

Deep learning, reinforcement learning, and world models

STaR: Bootstrapping Reasoning With Reasoning

Self-Consistency Improves Chain of Thought Reasoning in Language Models

Chain of Hindsight Aligns Language Models with Feedback

Shared computational principles for language processing in humans and deep language models

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks

Language Models are Few-Shot Learners

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents.

A formal Basis for the Heuristic Determination of Minimum Cost Paths

defined

Generalist Agent was defined by Reed et al. in 2022

Reinfoceement Learning Agent

Stanford cs234 lecture slide 19

Kenton et al. (2021)

"The Role of Learning in Autonomous Robots"(1991)

"Intelligence without reason"

"New approaches to Intelligence"

Building Brains for Bodies

"The Artificial Life Route to Artificial Intelligence Building Embodied, Situated Agent"

Shavit et al. (2023)

Agent AI

Multi-task learning

Massively Multi-task learning

MemWalker-interactive agent

"Architectures for Contex"

"The Dimensions of Context Space"

Recursively Self-Improving (RSI)

STOP]#stop). Adam Kalai explains insights from this technique in this [lecture about STOP

LLM Self-Improves its LLM

Textbook-like datasets with Exercises

TinyGSM

Interactive Agent Foundational Model

Resource-cloud

Artificial General Intelligence

Levels of AGI

high compared to human-level performance on multiple benchmarks despite incomplete AGI

incremental tasks and Discontinuous tasks

AlphaZero

Consciousness: Here, There but Not Everywhere

Perspetives on Consciousness by Chalmers

Ilya Sutskever defined a practicalconsciousness test

The free energy principle and cognitive agents

A Survey on Large Language Model based Autonomous Agents

LLM Powered Autonomous Agents

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

Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives

LLMs

podcast

openai.com

🏷オープンソースツールとアプリケーションの役割

LLMエージェントの機能と主要モジュール

LLMエージェントは、LLMアプリケーションを使用して、複雑なタスクを実行するエージェントであり、計画、メモリなどの主要モジュールを組み合わせたアーキテクチャを使用して機能します。計画モジュールはタスクを分解し、メモリモジュールは過去の相互作用を管理し、ツールは外部環境とやり取りします。

LLMエージェントのフレームワークと機能

LLMエージェントのフレームワークには、ユーザーリクエスト、エージェント/ブレイン、計画、メモリなどの主要コンポーネントが含まれます。計画モジュールはタスクを解決するためのステップを分解し、メモリモジュールは過去の相互作用を管理します。ツールは外部環境とやり取りし、エージェントが必要な情報を取得します。LLMエージェントは、計画とメモリを使用して動的な環境で操作し、過去の経験を活用して未来の行動を計画します。

オープンソースツールとアプリケーションの役割

LangChain

LangChainは、言語モデルによって動作するアプリケーションを開発するためのオープンソースフレームワークです。AIと機械学習の開発者が、大規模言語モデルをシームレスに統合できるようにします。

LLMStack

LLMStackは、独自のデータを使用してAIアプリ、チャットボット、エージェントを構築するためのオープンソースプラットフォームです。カスタムフロントエンドを通じて独自のユーザーエクスペリエンスを作成できます。

Chainlit

Chainlitは、ビジネスロジックとデータを組み込んだChatGPTのようなアプリケーションの作成を迅速化するためのオープンソースPythonパッケージです。非同期Pythonフレームワークで、アプリケーションの開発を加速します。

Superagent

Superagentは、クラウドプラットフォームで補完されたオープンソースフレームワークで、インフラストラクチャの懸念なしにChatGPTのようなAIアシスタントを簡単に展開できます。

Helicone

Heliconeは、GPTモデルを効果的にクエリするためのオープンソースフレームワークです。ジェネレーティブAIを活用するビジネス向けの可観測性プラットフォームとして機能します。

オープンソースツールとアプリケーションの役割

オープンソースツールとアプリケーションの役割には、Open Interpreter、LLama2.c、Fooocus、CodeLllama、Llama-gpt、OpenTF、Vall-E-X、AI Town、Seamless Communicationなどが含まれます。これらのプロジェクトは、LLMやLLMエージェントを使用して、さまざまなタスクやコミュニケーションを改善することを目指しています。

考察

これらのプロジェクトは、LLMやLLMエージェントを活用して、コミュニケーション、タスクの自動化、言語翻訳などの分野で革新をもたらす可能性があります。特に、FooocusやCodeLllamaのようなプロジェクトは、特定のタスクに焦点を当てたLLMを使用して、複雑な問題の解決やコード生成を支援することができます。また、Seamless Communicationのようなプロジェクトは、異なる言語間でのコミュニケーションを円滑にすることができます。これらの取り組みは、LLM技術の進化と産業への適用に期待が高まります。

github.com

https://aka.ms/autogen-dc

https://aka.ms/autogen-roadmap

agent-based-framework

agent-oriented-programming

medium.com

general LLMs aren’t well suited for

Seamless Communication

this October 30th to November 2nd. With ODSC West 2023

, you’ll enjoy talks, sessions, events, and more that squarely focus on this fast-paced field.

! Get your pass today

marktechpost.com

github.com

RCI Agent for MiniWoB++

promptingguide.ai

Chain of Thought (opens in a new tab)

Tree of Thoughts (opens in a new tab)

Wang et al., 2023 (opens in a new tab)

ReAct (opens in a new tab)

Reflexion (opens in a new tab)

GITM (opens in a new tab)

MRKL (opens in a new tab)

Toolformer (opens in a new tab)

Function Calling (opens in a new tab)

HuggingGPT (opens in a new tab)

Ma et al. (2023) (opens in a new tab)

Horton (2023) (opens in a new tab)

Generative Agents (opens in a new tab)

AgentSims (opens in a new tab)

Blind Judgement (opens in a new tab)

Ziems et al. (2023) (opens in a new tab)

ChemCrow (opens in a new tab)

Math Agents]assist researchers in exploring, discovering, solving and proving mathematical problems. [EduChat (opens in a new tab)

CodeHelp (opens in a new tab)

Mehta et al. (2023) (opens in a new tab)

ChatDev (opens in a new tab)

ToolLLM (opens in a new tab)

MetaGPT (opens in a new tab)

D-Bot (opens in a new tab)

IELLM (opens in a new tab)

Dasgupta et al. 2023 (opens in a new tab)

OS-Copilot (opens in a new tab)

LangChain (opens in a new tab)

AutoGPT (opens in a new tab)

Langroid (opens in a new tab)

AutoGen (opens in a new tab)

OpenAgents (opens in a new tab)

LlamaIndex (opens in a new tab)

GPT Engineer (opens in a new tab)

DemoGPT (opens in a new tab)

GPT Researcher (opens in a new tab)

AgentVerse (opens in a new tab)

Agents (opens in a new tab)

BMTools (opens in a new tab)

crewAI (opens in a new tab)

Phidata (opens in a new tab)

ALFWorld (opens in a new tab)

IGLU (opens in a new tab)

Tachikuma (opens in a new tab)

AgentBench (opens in a new tab)

SocKET (opens in a new tab)

AgentSims (opens in a new tab)

ToolBench (opens in a new tab)

WebShop (opens in a new tab)

Mobile-Env (opens in a new tab)

WebArena (opens in a new tab)

GentBench (opens in a new tab)

RocoBench (opens in a new tab)

EmotionBench (opens in a new tab)

PEB (opens in a new tab)

ClemBench (opens in a new tab)

E2E (opens in a new tab)

LLM Powered Autonomous Agents (opens in a new tab)

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning (opens in a new tab)

A Survey on Large Language Model based Autonomous Agents (opens in a new tab)

The Rise and Potential of Large Language Model Based Agents: A Survey (opens in a new tab)

Large Language Model based Multi-Agents: A Survey of Progress and Challenges (opens in a new tab)

Cognitive Architectures for Language Agents (opens in a new tab)

Introduction to LLM Agents (opens in a new tab)

LangChain Agents (opens in a new tab)

Building Your First LLM Agent Application (opens in a new tab)

Building LLM applications for production (opens in a new tab)

Awesome LLM agents (opens in a new tab)

Awesome LLM-Powered Agent (opens in a new tab)

Functions, Tools and Agents with LangChain (opens in a new tab)

turingpost.com

🏷最新の研究動向と実装事例

NutanixのLLMエージェント実装

Nutanixは、AI/MLをインフラストラクチャに統合し、LLMエージェントの実装を行っています。LLMモデルの状態クラス生成やアクション選択テンプレートなどの手順が示されており、推論とアクションの統合にはヒューリスティックや強化学習が活用されています。

LLMエージェントの実装における洞察

LLMエージェントの実装には、複数のステップが必要であり、透明性とトレース可能な推論が重要である。また、LLMエージェントは複数のアクションスレッドを管理し、CoTアプローチを活用して自律的にアクション選択を行うことができる。推論アクションループの統合には、ヒューリスティックや強化学習が効果的に活用されている。

NutanixのLLMエージェント実装に関連する詳細情報

NutanixはAI/MLをインフラストラクチャに統合し、LLMエージェントの実装を行っている。
LLMモデルの状態クラス生成やアクション選択テンプレートが示されている。
推論とアクションの統合にはヒューリスティックや強化学習が活用されている。
LLMエージェントは複数のアクションスレッドを管理し、CoTアプローチを活用して自律的にアクション選択を行う。
推論アクションループの統合には、ヒューリスティックや強化学習が効果的に活用されている。

nutanix.com

RAISEとAIOSの概要

RAISEは、GPT-4などの大規模言語モデル（LLMs）を会話エージェントに統合する能力を向上させるアーキテクチャであり、会話の文脈と連続性を維持します。初期評価では、RAISEが従来のエージェントに比べて利点を示し、AI分野に貢献しています。AIOSは、LLMエージェントのパフォーマンスと効率性を向上させるオペレーティングシステムであり、信頼性と効率性を実証しています。

RAISEとAIOSの考察

RAISEとAIOSは、LLMsを活用した会話エージェントとオペレーティングシステムの進化を示しています。RAISEは会話の文脈を維持し、AI分野に新たな貢献をもたらす一方、AIOSはエージェントのパフォーマンスと効率性を向上させることで、将来のAIOSエコシステムの発展を目指しています。両者は、大規模言語モデルを活用したエージェントシステムの発展において重要な役割を果たしており、AI技術の進化に貢献しています。

進化するオペレーティングシステム

オペレーティングシステム（OS）の進化は、単純なバッチジョブ処理から、タイムシェアリングやマルチタスク処理などの高度なプロセス管理技術へと進化してきました。OSの進化により、プロセススケジューリング、メモリ管理、ファイルシステム管理などの特定の責任が明確になり、効率性と管理性が向上しました。さらに、GUIの登場やOSエコシステムの拡大により、OSはよりインタラクティブでユーザーセントリックなものとなり、開発者ツールやランタイムライブラリを提供することで、ソフトウェア開発を促進し、OSアプリケーションエコシステムを繁栄させています。

大規模言語モデルエージェント

大規模言語モデル（LLM）ベースの自律エージェントは、複雑なタスクの解決に自然言語の指示を受け取ります。LLMベースのエージェントは、単一エージェントシステムと複数エージェントシステムに一般的に分類されます。単一エージェントシステムでは、旅行計画や個人向けの推薦、芸術的デザインなどの複雑なタスクを解決するために単一のLLMエージェントが使用されます。一方、複数エージェントシステムでは、複数のエージェントが相互作用し、協力的、競争的、またはその両方の関係を持って問題を解決します。

AIOSのレイヤー

AIOSのアーキテクチャは、アプリケーションレイヤー、カーネルレイヤー、ハードウェアレイヤーに分かれており、各レイヤーがシステム全体の責任を明確に分担しています。アプリケーションレイヤーでは、旅行エージェントや数学エージェントなどのエージェントアプリケーションが開発され、展開されます。カーネルレイヤーにはOSカーネルとLLMカーネルがあり、それぞれ非LLMおよびLLM固有の操作を担当しています。ハードウェアレイヤーは、CPU、GPU、メモリ、ディスクなどの物理コンポーネントから構成されており、LLMカーネルのシステムコールは直接ハードウェアとやり取りせず、OSのシステムコールを介してハードウェアリソースを管理します。

linkedin.com

large language models

can pass the Turing Test

arxiv.org

deeperinsights.com

source

LLM-powered agents

source

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

enhancing the reasoning of LLMs.

BabyAGI

AutoGPT

ChatGPT Plus

the potential for this technology

nutanix.com

™

chain-of-thought

action plan generation

current LLM literature

constitutional AI

chain-of-thought (CoT)

NVIDIA Management Library (NVML)

CUDA

SARSA

arxiv.org

https://github.com/agiresearch/AIOS

wooldridge1995intelligent,

geng2022recommendation,

dijkstra2002cooperating,

http://apple-history.com/128k

https://winworldpc.com/product/windows-3/31

https://www.gnome.org/

ge2023llm

https://developer.android.com/studio

https://developer.apple.com/xcode/

https://cloud.google.com/sdk

ge2023llm

ge2023openagi

ge2023openagi,

schick2023toolformer,

qian2023communicative

https://en.wikipedia.org/wiki/FIFO_(computing_and_electronics)

https://en.wikipedia.org/wiki/Round-robin_scheduling

Figure 4

https://en.wikipedia.org/wiki/Beam_search

thompson1984reflections,

https://openai.com/research/gpt-4

https://ai.facebook.com/blog/largelanguage-model-llama-meta-ai

https://courses.cs.washington.edu/courses/cse451/16wi/readings/lecture_readings/LCM_OperatingSystemsTimeline_Color_acd_newsize.pdf

https://github.com/langchain-ai/langchain

https://rapidapi.com/hub

https://blog.google/technology/developers/gemma-open-models/

🏷LLMエージェントの応用分野

LLMアプリケーションの応用分野

LLMアプリケーションの応用分野において、LLMを使用した新しいアーキテクチャやトレンドが注目されています。AIスタートアップやテクノロジー企業で使用されるリファレンスアーキテクチャやインコンテキストラーニング、ベクトルデータベース、オーケストレーションフレームワーク、ホスティング、AIエージェントフレームワークなどが重要な要素として挙げられています。

LLMアーキテクチャの新興トレンド

LLMアプリケーションの新しいアーキテクチャやトレンドは、AIの能力を拡大する大規模言語モデルによって新たな地平を開いています。これらのトレンドには、モデルのスケールに合わせた展開アーキテクチャの進化や、新しいMLOpsパターン、インフラ最適化、分散型アーキテクチャなどが含まれます。これらの革新を理解し、適切に活用することで、次世代のLLMベースのアプリケーションを構築する準備が整います。

LLMエージェントの応用分野に関連する詳細情報

[WSS23] LLMエージェントとの相互作用の調査 - Wolfram Community

Wolfram CommunityのStaff Picksで、LLMエージェントとの相互作用に関する投稿が編集コラムに選ばれ、Your ProfileでFeatured Contributor Badgeが表示されます。

LLMアーキテクチャの新興トレンド

LLMアーキテクチャの新興トレンドは、AIの能力を拡大する大規模言語モデルによって新たな地平を開いています。これらのトレンドには、モデルのスケールに合わせて展開アーキテクチャを進化させる、新しいMLOpsパターン、インフラ最適化、分散型、マルチモーダルアーキテクチャなどが含まれます。これらの革新を理解することで、次世代のLLMベースのアプリケーションを構築する準備が整います。

身体化されたLLMエージェントは組織されたチームで協力する方法を学ぶ

LLMエージェントは、推論、計画、意思決定に不可欠なツールとして台頭しています。LLMエージェントと人間エージェントの協力を通じて、指定されたリーダーシップがチームの効率に与える影響を示す一連の実験を通じて、LLMエージェントが示すリーダーシップの質と自発的な協力行動に光を当てています。

LLMを中心としたエージェントシステムの概要

LLMを中心とした自律エージェントシステムでは、LLMがエージェントの脳として機能し、計画、記憶、ツールの使用などの重要なコンポーネントで補完されます。これらの要素は、エージェントが複雑なタスクを効果的に処理し、問題解決能力を向上させるのに役立ちます。

arxiv.org

wolfram.com

http://wolfr.am/StaffPicks

Your Profile

Featured Contributor Board

github.io

(CoT; Chain of thought

summarization from human feedback

human preference dataset

(Locality-Sensitive Hashing): It introduces a

(Approximate Nearest Neighbors Oh Yeah): The core data structure are

(Hierarchical Navigable Small World): It is inspired by the idea of

small world networks

(Scalable Nearest Neighbors): The main innovation in ScaNN is

“External APIs” section

“Chain of thought prompting elicits reasoning in large language models.”

“Tree of Thoughts: Dliberate Problem Solving with Large Language Models.”

“LLM+P: Empowering Large Language Models with Optimal Planning Proficiency”

“ReAct: Synergizing reasoning and acting in language models.”

“Announcing ScaNN: Efficient Vector Similarity Search”

https://chat.openai.com/share/46ff149e-a4c7-4dd7-a800-fc4a642ea389

“Reflexion: an autonomous agent with dynamic memory and self-reflection”

“In-context Reinforcement Learning with Algorithm Distillation”

“MRKL Systems A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning.”

Why is Vector Search so fast?

“API-Bank: A Benchmark for Tool-Augmented LLMs”

“HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”

“ChemCrow: Augmenting large-language models with chemistry tools.”

“Emergent autonomous scientific research capabilities of large language models.”

“Generative Agents: Interactive Simulacra of Human Behavior.”

https://github.com/Significant-Gravitas/Auto-GPT

https://github.com/AntonOsika/gpt-engineer

a16z.com

in-context learning

Hyena paper

🏷セキュリティとプライバシーの考慮事項

LLMアプリケーションのセキュリティとプライバシーの考慮事項

LLMアプリケーションのセキュリティとプライバシーの考慮事項について、OWASPのトップ10脆弱性やプロンプトインジェクション、セキュアでない出力処理、トレーニングデータの改ざんなどが挙げられる。また、LLM Fine-Tuningにおけるプライバシーリスクとして、Input PrivacyとOutput Privacyのリスクがあることが指摘されている。

LLMアプリケーションのセキュリティとプライバシーの考慮事項に対する考察

LLMアプリケーションのセキュリティとプライバシーの考慮事項は重要であり、OWASPのリソースを活用することが勧められている。また、Fine-Tuningにおけるプライバシーリスクに対処するために、InputとOutputのプライバシーリスクを異なる技術を使用して管理することが重要である。データの保護とプライバシー確保が、LLMアプリケーションの設計と運用において重要な要素となる。

LLMアプリケーションのセキュリティとプライバシーの考慮事項に関連する有益な情報

LLMアプリケーションのセキュリティとプライバシーの考慮事項に関連する情報として、OWASPのトップ10脆弱性やFine-Tuningにおけるプライバシーリスクについての詳細な情報が提供されています。また、セキュリティとプライバシーを確保するための具体的な対策や技術についても言及されています。

OWASP Top 10 for LLM Applicationsでは、LLMアプリケーションにおける脆弱性や攻撃シナリオ、予防メカニズムについて詳細に説明されています。また、Fine-TuningにおけるInput PrivacyとOutput Privacyのリスクについても具体的に取り上げられています。

LLMアプリケーションのセキュリティとプライバシーの考慮事項に関する情報は、データの保護とプライバシー確保が重要であることを強調し、適切な対策を講じることが必要であることを示しています。

klu.ai

medium.com

Prompt Engineering

fine-tuning

generative and predictive

Fine-tuning LLM

NLU

, where the LLM returns highly plausible but incorrect answers. hallucination

relevant and contextual prompts

chain-of-thought

examples

Autonomous Agents

Python Math Libraries, Search and more

linkedin.com

OWASP Top 10 for LLM Applications

defense-in-depth blog post

mithrilsecurity.io

AI Total Cost of Ownership Calculator

Samsung’s data

Beyond Privacy Trade-offs with Structured Transparency

OpenAI recently introduced a fine-tuning feature that allows customization of their models for specific use cases

recent incident as described by OpenAI

Samsung’s data was leaked

Quantifying Memorization Across Neural Language Models

here

Privacy Enhancing Technologies

The Enterprise Guide to Adopting GenAI

BlindChat

open-source

audited on our Confidential AI stack

here

Discord

🏷将来の展望と課題

AIエージェントの新たな視点と自動化能力

AIエージェントは、AI技術の進化により新たな焦点として浮上し、自律的な意思決定や高度な自動化レベルへの移行を予示している。AIエージェントは、環境内で感知し、行動する能力を持ち、従来のLLMアプリケーションよりも複雑なタスクを自律的に実行する能力を持つ。彼らはワークフロー全体を再設計し、エンドツーエンドのワークフロー自動化を実現する可能性がある。

AIエージェントの台頭と産業への影響

AIエージェントの台頭は、技術アーキテクチャとエコシステムの見直しを必要とし、従来のロボティックプロセス自動化（RPA）を超える適応型自動化を提供する。彼らの影響は、産業全体に及び、ビジネスモデルやユーザーインタラクション方法の変化を促す。AIエージェントは、社会の行動や習慣にも影響を与え、新たな考え方や戦略が求められる。彼らは、技術の進歩だけでなく、社会経済的および文化的パターンの深い変革をもたらす可能性がある。

AIエージェントの未来展望と課題

AIエージェントの未来展望と課題に関連する情報を提供します。

AIOS-Agentエコシステムの概要

AIOS-Agentエコシステムは、大規模言語モデル（LLM）が（人工）知能オペレーティングシステム（IOSまたはAIOS）として機能し、AIエージェントアプリケーション（エージェントまたはAAP）が開発される革新的なエコシステムを描いています。これにより、AIOS-Agentエコシステムが豊かになり、従来のOS-APPエコシステムからのパラダイムシフトが示されます。LLMの影響はAIアプリケーションレベルに限定されず、コンピュータシステム、アーキテクチャ、ソフトウェア、プログラミング言語の設計と実装を革新するでしょう。

オープンソースLLMsの利点とプロジェクト・組織の種類

オープンソースLLMsの利点は透明性と柔軟性、コスト削減、追加機能とコミュニティの貢献が挙げられる。プロジェクトの種類はテキスト生成、コード生成、仮想チュータリングなど多岐にわたる。使用する組織はIBMやNASA、出版社、医療機関など多様である。

AgentBenchによるLLMエージェントの評価と課題

AgentBenchによるLLMエージェントの評価では、商用LLMsとOSS競合他社の性能に格差があることが示されている。失敗の理由は長期的な推論や意思決定能力の低さであり、トレーニングデータや環境の改善が重要である。エージェントのパフォーマンス向上のためには、データセットや統合評価パッケージの活用が必要である。

将来の展望と課題

IBMやNASAなどの組織は、オープンソースLLMsを活用して、気候変動対策やジオスペーシャルデータのトレーニングに取り組んでいる。
出版社やジャーナリストは、情報の分析や要約にオープンソースLLMsを使用しており、プロプライエタリなデータの共有を避けている。
医療機関は、医療ソフトウェアにおいて診断支援や治療最適化にオープンソースLLMsを導入している。
AgentBenchの発展により、LLMエージェントの性能向上や課題の克服が期待される。データセットや環境の改善により、エージェントの能力が向上し、実世界の使命に対応できるようになる。

openreview.net

www.ibm.com

arxiv.org

https://www.microsoft.com/en-us/windows/

https://www.apple.com/macos/

https://www.apple.com/ios/

https://www.android.com/

https://www.microsoft.com/en-us/microsoft-365/word/

https://www.google.com/docs/about/

https://www.microsoft.com/en-us/microsoft-365/outlook/

https://www.google.com/gmail/about/

1](#S0.F1). These tools empower application developers to efficiently design, implement, and run their applications within the operating system environment. For instance, the well-known iOS ecosystem includes a dedicated application development toolkit known as Xcode 11 1111 [https://developer.apple.com/xcode/

https://www.apple.com/app-store/

section 2.1.2](#S2.SS1.SSS2), the command-line interface used to be the narrow bridge between users to interact with OS. Since command lines are highly professional, it prevented broader groups of users from easily and efficiently operating the computers. From Xerox Alto introduced by Palo Alto Research Center (PARC) in 1973 13 1313 [https://spectrum.ieee.org/xerox-alto

http://apple-history.com/128k

https://winworldpc.com/product/windows-3/31

https://www.gnome.org/

https://www.kde.org/

https://unityd.org/

section 2.1.3](#S2.SS1.SSS3)). Similarly, embedded devices ranging from IoT to robotics pose the aspect of real-time responses and more strict resource management in OS, which has been reflected in many successful embedded OSes such as VxWorks 19 1919 [https://www.windriver.com/products/vxworks

https://blackberry.qnx.com/en

https://www.apple.com/siri/

https://www.microsoft.com/en-us/cortana

https://history-computer.com/atlas-computer/

https://en.wikipedia.org/wiki/History_of_Unix

https://www.magzter.com/stories/technology/Gadgets-Philippines/ARPANET

https://en.wikipedia.org/wiki/Windows_1.0x

https://www.brookings.edu/articles/chatgpt-educational-friend-or-foe/

https://github.com/agiresearch/OpenAGI

https://github.com/joonspk-research/generative_agents

https://openai.com/research/gpt-4v-system-card

1](#S3.T1), respectively. Taking the example of the ecosystem in modern Linux operating system–the most widely-used open source operating system community by now, here are the successful experiences in its evolving history. Specifically, a rich set of built-in and third-party libraries from thousands of experts and open-source developers leads to the success of the Linux ecosystem. Managing the install/uninstall and dependencies of those libraries, along with the versioning for tracking software development, is critical. The Linux ecosystem, over the past few decades, provides library management tools such as dpkg 25 2525 [https://man7.org/linux/man-pages/man1/dpkg.1.html

http://yum.baseurl.org/

https://git-scm.com/

https://www.gnu.org/software/bash/

https://nvd.nist.gov/vuln/detail/cve-2017-0143

https://nvd.nist.gov/vuln/detail/cve-2010-0188

https://krebsonsecurity.com/2018/03/who-and-what-is-coinhive/

https://github.com/hwchase17/langchain

https://doi.org/10.1145/1478873.1478950

https://www.computeexpresslink.org/

https://news.agpt.co/

https://pubs.opengroup.org/onlinepubs/9699919799.2018edition/

https://doi.org/10.5281/zenodo.1234

https://docs.kernel.org/scheduler/sched-design-CFS.html

https://doi.org/10.1109/MSECP.2003.1236233

https://doi.org/10.1145/361011.361061

https://doi.org/10.1109/AINA.2010.81

https://github.com/tatsu-lab/stanford_alpaca

https://courses.cs.washington.edu/courses/cse451/16wi/readings/lecture_readings/LCM_OperatingSystemsTimeline_Color_acd_newsize.pdf

https://princeton-nlp.github.io/language-agent-impact/

linkedin.com

Generative AI: A Corporate Guide to Embracing the Technological Revolution

ICAREX

GenerazioneAI

medium.com

🖍 考察

結果の確認

LLMエージェントの有名な論文やOSSについての調査から、AI技術の発展における重要な課題や可能性について幅広い視点が提供されています。特に、AIの危険性や限界に関する研究は、技術の進化と社会への影響を考える上で重要な示唆を与えています。また、LLMエージェントのフレームワークやプロジェクトに関する情報も豊富に含まれており、革新的なアプリケーションや取り組みがAI技術の進化にどのように貢献しているかが明らかになっています。

重要性と影響の分析

調査結果から得られる情報は、AI技術の将来における重要な方向性や課題を示しています。これらの研究成果は、AIの倫理的な問題や技術の限界に対する理解を深める上で重要であり、今後の研究や開発に大きな影響を与える可能性があります。また、既存の仮説や他の応用例との比較を通じて、今回の調査結果がどれだけ革新的で重要かを評価することが重要です。

ネクストステップの提案

調査から生じた疑問点や未解決の課題に対処するために、さらなる研究や行動計画が必要です。特に、AI技術の倫理的な側面や社会への影響に関する研究をさらに深めることが重要です。また、AI技術の発展に伴う課題や可能性について、産業や社会にどのような影響を与えるかを検討することも重要です。

今後の調査の方向性

今回の調査における限界点を踏まえて、AI技術のさらなる発展に向けて新たな調査のテーマを提案します。例えば、AI技術の倫理的な問題や社会的影響に焦点を当てた研究や、AI技術を活用した革新的なアプリケーションの開発に関する調査が重要です。さらに、AI技術の進化に伴う新たな課題や可能性について、継続的な情報収集と分析を行うことが重要です。

📖 レポートに利用された参考文献

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117件の参考文献から33件の情報を精査し、約165,000語の情報を整理しました。あなたは約14時間の調査時間を削減したことになります🎉

調査された文献

117件

精査された情報

33件

整理された情報量

約165,000語

削減された時間

約14時間

🏷 導入: LLMエージェントとは

zjunlp/LLMAgentPapers: Must-read Papers on LLM Agents. - GitHub

Must-read Papers on Large Language Model Agents. "Here are some other paper lists you might be interested in: Prompt4ReasoningPapers: Reasoning with ...

github.com

Introduction to LLM Agents | NVIDIA Technical Blog

To describe agents a bit more, here's the general architecture of an LLM-powered agent application (Figure 1). Architecture diagram of an LLM- ...

nvidia.com

What is LLM Agent? Ultimate Guide to LLM Agent [With Technical ...

LLM Agents Application. 1. Autogpt. An open-source Python program, Auto GPT, was developed from the ground up on the same framework as GPT-4. It was recently ...

ionio.ai

How I developed my first LLM-Agent based application | by Lucasvittal

So I firstly build the schema, after that, the architecture and finally the backlog. To facilitate the backlog construction, I divided the ...

medium.com

🏷 GPTや他のLLMに関する論文の重要性

Autonomous Agents (LLMs) research papers. Updated Daily. - GitHub

LLM agent includes roles: requirements engineer, architect, developer, tester and scrum master. Uses same prompt, with role-identifier, role-specific ...

github.com

Foundational must read GPT/LLM papers - OpenAI Developer Forum

Initializing a new thread on the very best, must read, well-written, papers on Large Language Model capabilities, limits, and use.

openai.com

[PDF] A Survey on Large Language Model based Autonomous Agents

When comparing LLM-based autonomous agents to traditional machine learning, designing the agent architecture is analogous to determining the ...

arxiv.org

Awesome-LLM: a curated list of Large Language Model - GitHub

Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM, ...

github.com

🏷 オープンソースツールとアプリケーションの役割

LLM Agents - Prompt Engineering Guide

... LLM-based agents in various applications. Agents (opens in a new tab): an open-source library/framework for building autonomous language agents. The library ...

promptingguide.ai

10 open-source tools for LLM applications development - Turing Post

LLMStack: An open-source platform for building AI apps, chatbots, and agents using proprietary data. Chainlit: An open-source Python package ...

turingpost.com

10+ Open-Source Tools for LLM Applications Development

LeMUR is a user-friendly platform that simplifies the development of LLM applications on spoken data. It empowers developers to perform diverse ...

marktechpost.com

llm-agent · GitHub Topics

The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). It provides a simple yet robust interface using llama- ...

github.com

A curated list of awesome LLM agents. - GitHub

A curated list of awesome LLM agents. Open source, application, platform, or resources. If you have a suggestion, feel free to open an issue or pull request.

github.com

9 Open Source LLMs and Agents to Watch | by ODSC

So let's take a look at some interesting and new open-source LLMs and LLM agents that we are following: Open Interpreter: Open Interpreter is a ...

medium.com

🏷 最新の研究動向と実装事例

From LLM to Conversational Agent: A Memory Enhanced ... - arXiv

This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large ...

arxiv.org

LLM Agent Operating System - arXiv

We present the architecture of such an operating system, outline the core challenges it aims to resolve, and provide the basic design and ...

arxiv.org

Impact of Large Language Models on Enterprise: Benefits, Risks ...

Overview of important LLM tools and foundation models set to impact on the market. Hopefully, this article will help you navigate the hurdles ...

linkedin.com

The Intelligent Agents of Tomorrow: A Guide to LLM-Powered Agents

Explore the world of LLM-powered AI agents, their capabilities in task execution, information gathering, and communication.

deeperinsights.com

It's Time For AI: How Nutanix Implemented an LLM Agent

In this article, we present a Large Language Model (LLM) agent, engineered to perform both language generation and decision tracing .

nutanix.com

🏷 LLMエージェントの応用分野

Emerging Architectures for LLM Applications | Andreessen Horowitz

A reference architecture for the LLM app stack. It shows the most common systems, tools, and design patterns used by AI startups and tech ...

a16z.com

Emerging Trends in LLM Architecture | by Bijit Ghosh - Medium

Overview of LLM Architecture Trends ; Deployment Architecture — New patterns to serve LLMs with low latency at scale ; Data Architecture — ...

medium.com

[WSS23] Investigating LLM-agent interactions - Wolfram Community

The project is composed of three major steps: Firstly, we generate LLM-based societies of increasing complexity, translating real-world societal structures and ...

wolfram.com

Embodied LLM Agents Learn to Cooperate in Organized Teams

This research studies the integration of prompt-based organizational structures to teams of LLM agents, contributing to more efficient and ...

arxiv.org

LLM Powered Autonomous Agents | Lil'Log

Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, ...

lilianweng.github.io

🏷 セキュリティとプライバシーの考慮事項

Security Considerations When Building LLM Applications - LinkedIn

8. Excessive agency: Similar to point 7, if you allow an LLM and its agents to perform tasks that deal with data or other applications, there ...

linkedin.com

Privacy Risks of LLM Fine-Tuning - Mithril Security Blog

LLMs can inadvertently disclose confidential data through two main pathways - input privacy breaches when data is exposed to third-party AI ...

mithrilsecurity.io

Emerging Large Language Model (LLM) Application Architecture

Due to the highly unstructured nature of Large Language Models (LLMs), there are thought and market shifts taking place on how to implement LLMs ...

medium.com

Emerging Architectures for LLM Applications - Klu.ai

Three pivotal trends are also shaping the trajectory of LLM applications: Federated Learning, Sparse Attention Mechanisms, and New Model Architectures.

klu.ai

🏷 将来の展望と課題

Future Trends in the AI Industry: Parallel Evolution of LLM and AI ...

This article explores the concurrent evolution of LLM and AI Agents, examining their joint role in driving tech innovation and forecasting their ...

medium.com

The Rise of AI Agents - Understanding Their Role and Integrating ...

In the current context, characterized by rapid technological evolution and an increasing demand for innovative solutions, AI Agents emerge as ...

linkedin.com

LLM as OS, Agents as Apps: Envisioning AIOS, Agents and ... - arXiv

The study provides a rich array of scenarios that serve as a valuable resource for future research into the alignment of LLM-based Agents.

arxiv.org

AgentBench: Evaluating LLMs as Agents - OpenReview

We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's ...

openreview.net

Open source large language models: Benefits, risks and types - IBM

An open source LLM offers transparency regarding how it works, its architecture and training data and methodologies, and how it's used.

ibm.com

📖 レポートに利用されていない参考文献

検索結果: 57件追加のソース: 0件チャット: 0件

13 Not-to-Miss Research Papers on LLMs - Analytics India Magazine

If you're eager to delve into the realm of LLMs and chatbots, exploring their origins and architecture, look no further.

analyticsindiamag.com

Sanyam Bhutani on LinkedIn: My favourite LLM paper is finally open ...

My favourite LLM paper is finally open source! Stanford University had published a paper running a simulation of Generative Agents which ...

linkedin.com

FinMem: A Performance-Enhanced LLM Trading Agent with Layered ...

Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains.

paperswithcode.com

hyp1231/awesome-llm-powered-agent - GitHub

Awesome LLM-Powered Agent · General Reasoning & Planning & Tool Using · Multi-Agent Cooperation · Framework & Open-Source · Trustworthy.

github.com

RSS3 Open-Source AI Architecture - turn any LLM into Web3 AI ...

With OpenAgent, customized AI agents can be created for specific needs such as analysis, prediction, content creation, and transaction ...

cryptoslate.com

AgentLM - Building an open source LLM agent - LinkedIn

AgentInstruct dataset was constructed for 6 tasks (AlfWorld, WebShop, Mind2Web, Knowledge Graph, Operating System and Database) in 3 stages -.

linkedin.com

Exploring 5 Open Source LLMs and Agents Shaping Conversational ...

The world of open source Language Models (LLMs) and conversational agents continues to expand, bringing forth innovative solutions and ...

medium.com

Navigating the AI Landscape: How Leveraging LLMs Can Drive ...

AI is disrupting the tech industry in a myriad of ways, with the introduction of tools like ChatGPT, Midjourney, and DALL·E seeing millions of users every ...

applydigital.com

The Future of LLM: Harnessing its Potential in Advance Technology

Impact of LLM On Emerging Technologies. As AI drives more attention in the digital economy, Organizations started focusing on intellectual property ...

smartdatainc.com

Generative AI Market Size, Share, Trends | LLMs Industry Impact

The Generative AI market report covers the technology roadmap till 2030, with insights into the short-term, mid-term and long-term developments.

marketsandmarkets.com

7 LLM Use Cases 2024 | *instinctools

Get a breakdown on the benefits, risks, and real-world LLM use cases to explore the potential of generative AI for your company.

instinctools.com

AI and LLMs in Finance: Opportunities, Challenges, and the Road ...

This week, we're going to explore a specific case of using AI and LLMs in the financial industry and how it might impact the future. In a world ...

medium.com

A Venture Capitalist's Guide to Agent Startups: LLM Integrations ...

Explore the critical role of venture capitalists in the AI startup ecosystem, focusing on investments in LLM and generative AI technologies like ChatGPT, ...

skimai.com

AI and Disruptive Innovation - Towards Data Science

AI will have a major impact on the global economy. It is estimated that the market for predictive analytics software will amount to more than $6.5 billion ...

towardsdatascience.com

Comparing the best: Top 5 open-source LLMs - n8n Blog

There is no single best open-source LLM. And here's why. There are many benchmarks for rating the models, and various research groups decide for ...

n8n.io

[PDF] Comparing Single and Multi-agent LLMs - arXiv

We compare two LLM structures: a single LLM and a multi-agent LLM architecture. We compare their abilities to (1) create realistic strategies, ...

arxiv.org

(PDF) AIOS: LLM Agent Operating System - ResearchGate

Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems ...

researchgate.net

8 Top Open-Source LLMs for 2024 and Their Uses - DataCamp

Discover some of the most powerful open-source LLMs and why they will be crucial for the future of generative AI.

datacamp.com

Mistral AI vs. Meta: Comparing Top Open-source LLMs

In this article, we explain in more detail each of the novelty concepts that Mistral AI added to traditional Transformer architectures and we ...

towardsdatascience.com

(PDF) A Review on Large Language Models: Architectures ...

However, this review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained ...

researchgate.net

[PDF] BOLAA: BENCHMARKING AND ORCHESTRATING LLM ...

Therefore, we provide a comprehensive comparison of LAA in terms of both agent architectures and LLM backbones. Additionally, we propose a new strategy to ...

openreview.net

10 Trends in LLM Dev Business Owners Need To Watch in 2024

Look at the top 10 trends in large language model development, including the rise of multimodal LLMs. Consider the future of LLMs for your ...

techopedia.com

Four LLM trends since ChatGPT and their implications for AI builders

LLMs are getting operational with plugins, agents and frameworks. The big challenges of LLM training being roughly solved, another branch of ...

towardsdatascience.com

The Future of Generative AI Agents - Foundation Capital

LLMs represent a new paradigm for agent design. Historically, agent design has leaned on rule-based systems, such as finite-state machines and ...

foundationcapital.com

Multimodality, Tool Use, and Autonomous Agents: Large Language ...

In this explainer, we described how LLMs and related systems are already much more than conversationalists, and can be used to generate ...

georgetown.edu

Unpacking the New Trends: Machine Learning, AI, Neural Networks ...

Unpacking the New Trends: Machine Learning, AI, Neural Networks & the Rise of LLM's.

linkedin.com

Open challenges in LLM research - Chip Huyen

Open challenges in LLM research · 1. Reduce and measure hallucinations · 2. Optimize context length and context construction · 3. Incorporate other ...

huyenchip.com

AIOS: LLM Agent Operating System - arXiv

This paper proposes the AIOS architecture, demonstrating the potential to facilitate the development and deployment of LLM-based agents, ...

arxiv.org

benchmarking LLM agents on consequential real-world tasks

We hope that having more benchmarks measuring how well current LLM agents perform on very difficult real-world tasks will help researchers come to greater ...

openphilanthropy.org

A Taxonomy for Autonomous LLM-Powered Multi-Agent Architectures

This paper proposes a comprehensive multi-dimensional taxonomy, engineered to analyze how autonomous LLM-powered multi-agent systems balance the ...

researchgate.net

Salesforce AI Researchers Introduce the Evolution of LLM ...

Salesforce AI Researchers Introduce the Evolution of LLM-Augmented Autonomous Agents and the Innovative BOLAA Strategy.

marktechpost.com

LLM-Powered Applications' Architecture Patterns and Security ...

We examine the significance of big data, innovative model architectures, and immense computing power in unlocking the infinite possibilities of ...

medium.com

What are top open source projects in LLM space : r/LocalLLaMA

What are top open source projects in LLM space · SillyTavern frontend · koboldcpp backend (for GGML/GGUF models) · oobabooga's text-generation- ...

reddit.com

Open Source AI Agents to Watch - MLQ.ai

This agent enables multi-agent conversations to solve tasks for the user. AutoGen is a framework that enables the development of LLM applications using multiple ...

mlq.ai

New OPEN SOURCE Software ENGINEER Agent Outperforms ALL ...

How To Not Be Replaced By AGI https://youtu.be/AiDR2aMye5M Stay Up To Date With AI Job ...

youtube.com

Flowise - Low code LLM Apps Builder

Open source low-code tool for developers to build customized LLM orchestration flow and AI agents.

flowiseai.com

Private LLMs: Data Protection Potential and Limitations - Skyflow

This post explains private LLMs and their potential and limits amid rising AI privacy concerns. - Mar 05, 2024 - By.

skyflow.com

Security Risks Of Generative Al Open Source Software

The vulnerability here is that the output of the LLM is passed directly to an exec statement, which will execute the code snippet. It should be ...

robustintelligence.com

LLM Agents can Autonomously Hack Websites - arXiv

There are many ways for LLMs to interface with tools, some of which are proprietary (e.g., OpenAI's). Report issue for preceding element.

arxiv.org

LLMs Pose Major Security Risks, Serving As 'Attack Vectors' - C3 AI

Researchers uncover AI security vulnerabilities using open source LLMs, highlighting the importance of safeguarding against AI privacy risks ...

c3.ai

Open source AI chatbot privacy issues addressed by Sendbird

We recently caught up with John S. Kim and discussed open-source large language models and privacy issues with open-source LLMs.

appdevelopermagazine.com

Security and Privacy: Closed Source vs Open Source Battle - Medium

The challenges posed by AI regarding data privacy and security are substantial, but not insurmountable. As AI continues to evolve, so must our ...

medium.com

[PDF] A Survey on Large Language Model (LLM) Security and Privacy

The Ugly (§6): We explore the vulnerabilities and defenses in LLMs, categorizing vulnerabilities into two main groups: AI Model Inherent ...

arxiv.org

Security concerns · Issue #1026 · langchain-ai/langchain - GitHub

I have some concerns about the way some of this code is implemented. To name the two I've noticed so far, the llm_math and sql_database ...

github.com

Generative AI Architectural Patterns - LinkedIn

A short primer on the 5 most prevalent Generative AI architectural patterns today: Black-box LLM APIs. Enterprise Apps in LLM App Store.

linkedin.com

[PDF] arXiv:2402.15538v1 [cs.MA] 23 Feb 2024

agent architecture designs consolidates the fast development of LLM agents. As such, a lightweight, easy-to-use library for efficiently ...

arxiv.org

A Safety-focused LLM Constellation Architecture for Healthcare - arXiv

The case for AI-based Healthcare Agents. The US healthcare industry is facing a massive shortage of healthcare workers, that became even more ...

arxiv.org

Architecting the future of AI agents: 5 flexible conversation ...

Let's explore how powerful agents are designed based on these frameworks. Global framework—the architect's blueprints.

voiceflow.com

LLM Risks: Insights & Real-World Case Studies - Akto

LLM security involves protecting AI systems like ChatGPT, Bard from potential risks such as biased outputs, malicious use and maintaining privacy in their ...

akto.io

Multi-Agent LLM Applications | A Review of Current Research, Tools ...

Research into agent-based systems has encompassed the study of robots, with a focus on path planning, robot navigation, and the Sense-Plan-Act ...

victordibia.com

Building Your First LLM Agent Application | NVIDIA Technical Blog

In cases where you must build complex agents ... The choice of OSS frameworks depends on the type of ... LLM-Powered Data Agent for Data Analysis.

nvidia.com

Part 1 - Generalized Architecture for LLM API's in Client Applications

The below diagram presents a generalized inference architecture designed for the integration of Language Model (LLM) APIs within a client ...

linkedin.com

Future of Coding — Multi-Agent LLM Framework using LangGraph

In this we first define the architecture flow and different Agents. We specialize these agents in specific tasks and assign the role. High Level ...

medium.com

An LLM-agent Collaboration Framework with Agent Team Optimization

In this work, we propose to construct a strategic team of agents communicating in a dynamic interaction architecture based on the task query. Specifically, we ...

openreview.net

Markus Stoll - os #llm #agent #agi #github - LinkedIn

Here's a breakdown of the architecture: Agent ... architectural design and - data utilization. ... You can find more details and an example ...

linkedin.com

Building RAG-based LLM Applications for Production - Anyscale

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on ...

anyscale.com

AI Agents: When and How To Implement - Arize AI

Delve into the intricacies of AI agents in the LLMOps era. Explore five pivotal considerations for AI agent implementation, ...

arize.com

📊 ドメイン統計

参照ドメイン数: 49引用済み: 19総文献数: 117

arxiv.org

引用: 5件/ 総数: 13件

引用率: 38.5%

medium.com

引用: 5件/ 総数: 13件

引用率: 38.5%

github.com

引用: 5件/ 総数: 9件

引用率: 55.6%

linkedin.com

引用: 3件/ 総数: 10件

引用率: 30.0%

a16z.com

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引用率: 16.7%

nvidia.com

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引用率: 20.0%

promptingguide.ai

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引用率: 20.0%

openreview.net

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引用率: 33.3%

nutanix.com

引用: 1件/ 総数: 2件

引用率: 50.0%

marktechpost.com

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lilianweng.github.io

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引用率: 50.0%

openai.com

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ionio.ai

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deeperinsights.com

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wolfram.com

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turingpost.com

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mithrilsecurity.io

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klu.ai

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ibm.com

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researchgate.net

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towardsdatascience.com

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foundationcapital.com

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mlq.ai

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youtube.com

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flowiseai.com

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skyflow.com

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robustintelligence.com

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c3.ai

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appdevelopermagazine.com

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voiceflow.com

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arize.com

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新しいテーマを調査する

運営会社サービス概要メディア

サインイン

LLMエージェントの有名な論文やOSSについての調査

📜 要約

主題と目的の要約

主要な内容と発見

結果と結論のまとめ

🔍 詳細

🏷導入: LLMエージェントとは

LLMエージェントの概要

LLMエージェントの開発と研究

LLMエージェントの事例と研究論文

🏷GPTや他のLLMに関する論文の重要性

GPTや他のLLMに関する論文の重要性の要約

考察

大規模言語モデルベースの自律エージェントに関する調査

🏷オープンソースツールとアプリケーションの役割

LLMエージェントの機能と主要モジュール

LLMエージェントのフレームワークと機能

オープンソースツールとアプリケーションの役割

LangChain

LLMStack

Chainlit

Superagent

Helicone

オープンソースツールとアプリケーションの役割

考察

🏷最新の研究動向と実装事例

NutanixのLLMエージェント実装

LLMエージェントの実装における洞察

NutanixのLLMエージェント実装に関連する詳細情報

RAISEとAIOSの概要

RAISEとAIOSの考察

進化するオペレーティングシステム

大規模言語モデルエージェント

AIOSのレイヤー

🏷LLMエージェントの応用分野

LLMアプリケーションの応用分野

LLMアーキテクチャの新興トレンド

LLMエージェントの応用分野に関連する詳細情報

[WSS23] LLMエージェントとの相互作用の調査 - Wolfram Community

LLMアーキテクチャの新興トレンド

身体化されたLLMエージェントは組織されたチームで協力する方法を学ぶ

LLMを中心としたエージェントシステムの概要

🏷セキュリティとプライバシーの考慮事項

LLMアプリケーションのセキュリティとプライバシーの考慮事項

LLMアプリケーションのセキュリティとプライバシーの考慮事項に対する考察

LLMアプリケーションのセキュリティとプライバシーの考慮事項に関連する有益な情報

🏷将来の展望と課題

AIエージェントの新たな視点と自動化能力