Podcast
Questions and Answers
What is the primary first step that Captain Agent takes after being given a task?
What is the primary first step that Captain Agent takes after being given a task?
- Generate a report outlining required tools
- Output the results of the task completion
- Reflect on the team's performance
- Identify a subtask and create a team of agents (correct)
How does Captain Agent equip its team of agents?
How does Captain Agent equip its team of agents?
- Using predefined tools retrieved from the tool library (correct)
- By allowing agents to generate their own tools
- By selecting random tools from the library
- Through a predefined list of tools unrelated to the task
What does the reflector LLM provide after the team of agents attempts to solve the subtask?
What does the reflector LLM provide after the team of agents attempts to solve the subtask?
- A success report indicating completion
- Recommendations for new agents
- An evaluation of the task difficulty
- A reflection report to guide further action (correct)
Which process is used by Captain Agent to generate agents for the subtasks?
Which process is used by Captain Agent to generate agents for the subtasks?
What type of agents does Captain Agent retrieve based on the role description?
What type of agents does Captain Agent retrieve based on the role description?
What is the primary responsibility of the User Proxy Agent?
What is the primary responsibility of the User Proxy Agent?
Which backbone is used by the HuggingFace Agent?
Which backbone is used by the HuggingFace Agent?
What dataset is mentioned in relation to initializing the agent library?
What dataset is mentioned in relation to initializing the agent library?
How is fairness in evaluation ensured for the methods being compared?
How is fairness in evaluation ensured for the methods being compared?
Which of the following categories is NOT included in the tool library?
Which of the following categories is NOT included in the tool library?
What happens to the agent library during the main experiment?
What happens to the agent library during the main experiment?
What is the primary purpose of the callable Python functions in the tool library?
What is the primary purpose of the callable Python functions in the tool library?
What is the required default response from the User Proxy Agent when a problem is deemed solved?
What is the required default response from the User Proxy Agent when a problem is deemed solved?
Which method achieved the highest average accuracy across different scenarios?
Which method achieved the highest average accuracy across different scenarios?
What is the average accuracy of the AutoGen method in the real-world scenarios?
What is the average accuracy of the AutoGen method in the real-world scenarios?
In the world-information retrieval scenario, which method displayed the lowest performance at Level 3?
In the world-information retrieval scenario, which method displayed the lowest performance at Level 3?
Which of the following methods had a lower accuracy in Mathematics compared to Captain Agent?
Which of the following methods had a lower accuracy in Mathematics compared to Captain Agent?
What was the accuracy of the Warm-up Act at Level 1 in the world-information retrieval scenario?
What was the accuracy of the Warm-up Act at Level 1 in the world-information retrieval scenario?
Which method had the highest accuracy in Programming tasks?
Which method had the highest accuracy in Programming tasks?
What unique advantage does Captain Agent provide compared to other methods in the context of accuracy?
What unique advantage does Captain Agent provide compared to other methods in the context of accuracy?
What is the achievement of the Captain Agent in relation to the success token method described?
What is the achievement of the Captain Agent in relation to the success token method described?
Which method showed the highest average accuracy across the three levels in the world-information retrieval scenario?
Which method showed the highest average accuracy across the three levels in the world-information retrieval scenario?
In the comparison of different LLM backbones, which backbone achieved the highest accuracy in Mathematics?
In the comparison of different LLM backbones, which backbone achieved the highest accuracy in Mathematics?
Which selection resulted in the lowest accuracy in Data Analysis?
Which selection resulted in the lowest accuracy in Data Analysis?
What was the average accuracy of the Adaptive Team (Captain Agent) across all subjects?
What was the average accuracy of the Adaptive Team (Captain Agent) across all subjects?
Which model reported an accuracy of 39.62 in Chemistry using the Tool Library?
Which model reported an accuracy of 39.62 in Chemistry using the Tool Library?
Which backbone achieved the lowest accuracy in Physics?
Which backbone achieved the lowest accuracy in Physics?
In the data provided, which option represents the best result marked in red bold for the adaptive team with the gpt-4-0125-preview model?
In the data provided, which option represents the best result marked in red bold for the adaptive team with the gpt-4-0125-preview model?
What was the average accuracy of the Static Team across all subjects?
What was the average accuracy of the Static Team across all subjects?
What is the main task regarding the BBC Earth video titled 'Top 5 Silliest Animal Moments'?
What is the main task regarding the BBC Earth video titled 'Top 5 Silliest Animal Moments'?
Who is responsible for verifying the accuracy of the bird identification?
Who is responsible for verifying the accuracy of the bird identification?
What is one of the first steps that the Digital Media Analyst takes in the task?
What is one of the first steps that the Digital Media Analyst takes in the task?
Which expert is primarily tasked with watching the video for bird identification?
Which expert is primarily tasked with watching the video for bird identification?
What is the expected output after completing the task related to the video?
What is the expected output after completing the task related to the video?
What is the primary focus of the paper 'Iterative forward tuning boosts in-context learning in language models'?
What is the primary focus of the paper 'Iterative forward tuning boosts in-context learning in language models'?
What concept is discussed in 'Why can GPT learn in-context?'?
What concept is discussed in 'Why can GPT learn in-context?'?
What do the authors of 'Transformers as algorithms' suggest about generalization in in-context learning?
What do the authors of 'Transformers as algorithms' suggest about generalization in in-context learning?
What innovative approach does 'Adaplanner' present in relation to language models?
What innovative approach does 'Adaplanner' present in relation to language models?
Which advancement is highlighted in 'Chain of thought prompting elicits reasoning in large language models'?
Which advancement is highlighted in 'Chain of thought prompting elicits reasoning in large language models'?
What is the contribution of 'Toolformer' in relation to language models?
What is the contribution of 'Toolformer' in relation to language models?
The research presented in 'Large language models as tool makers' mainly focuses on what aspect of language models?
The research presented in 'Large language models as tool makers' mainly focuses on what aspect of language models?
What does 'Travelplanner' aim to evaluate in the context of language models?
What does 'Travelplanner' aim to evaluate in the context of language models?
Flashcards
Captain Agent's Workflow
Captain Agent's Workflow
Captain Agent receives a task, plans, and repeatedly executes subtasks using agent teams, then gets feedback before concluding.
Subtask Identification
Subtask Identification
Captain Agent determines a smaller portion of a larger task to be executed by a team of agents.
Agent Team Building
Agent Team Building
Captain Agent assembles a team of agents by retrieving, selecting, and creating agents based on roles needed for a task.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation)
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Agent Roles
Agent Roles
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User Proxy Agent
User Proxy Agent
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Captain Agent
Captain Agent
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Adaptive Build
Adaptive Build
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Nested Conversation Reflection
Nested Conversation Reflection
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Agent Library
Agent Library
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Tool Library
Tool Library
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ConversableAgent Interface
ConversableAgent Interface
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Freeform Coding
Freeform Coding
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AutoAgents
AutoAgents
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Vanilla LLM
Vanilla LLM
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Meta-prompting
Meta-prompting
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Accuracy
Accuracy
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Problem-solving
Problem-solving
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Real-world Scenarios
Real-world Scenarios
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World-information Retrieval
World-information Retrieval
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Static Team
Static Team
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Adaptive Team
Adaptive Team
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Ablation Study
Ablation Study
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LLM Backbone
LLM Backbone
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Agent Team
Agent Team
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In-context learning
In-context learning
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Few-shot learning
Few-shot learning
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Gradient Descent as Meta-Optimizer
Gradient Descent as Meta-Optimizer
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Chain of Thought Prompting
Chain of Thought Prompting
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Tool Learning
Tool Learning
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Toolformer
Toolformer
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Language Models as Tool Makers
Language Models as Tool Makers
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Customizing LLMs with Specialized Toolsets
Customizing LLMs with Specialized Toolsets
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Study Notes
Adaptive In-conversation Team Building for Language Model Agents
- Static Build: Teams are built before task execution, containing all required expertise. This can be challenging to manage larger, more complex tasks as more team members are needed.
- Adaptive Build: Teams are built dynamically during the task, adjusting to the needs of each step. This approach is more flexible, utilizes nested group conversations and reflection to maintain diverse expertise and prevent repetitive outputs. This method leverages a "Captain Agent" that dynamically builds, manages, and maintains teams for each task phase.
Abstract
- Adaptive team-building paradigm: A novel approach for handling complex tasks using multiple LLM agents.
- Captain Agent: A new agent design that dynamically forms and manages teams for each step of a task, using nested conversations and reflection for diverse expertise and avoids repeated outputs.
- Evaluation: Across six real-world scenarios, Captain Agent significantly outperforms existing multi-agent methods, showing a 21.94% improvement in average accuracy without requiring scenario-specific prompt engineering.
Introduction
- Large Language Models (LLM): Capabilities in in-context learning, planning, tool-use, and conversation make them suitable for multi-agent systems.
- Multi-agent Systems: Mimicking human team building and collaboration abilities in multiple LLM agents.
- Human Team Building: Involves communication, social cognition, problem-solving, social learning, and shared intentionality. This enables effective team formation and problem-solving.
- Problem: Building an effective team of LLM agents for a given task remains a challenge.
Adaptive In-conversation Team Building
- Key components: Adaptive multi-agent team-building and nested group conversation with a reflection mechanism.
- Workflow: Captain Agent is prompted to create a plan for the task, which goes through iterative steps, building an agent team, having them solve decomposed subtasks using tool-assisted conversation, reflecting on the team performance, and either adjusting team composition or instructions until the task is complete.
Evaluation
- Scenarios: Mathematics, programming, data analysis, world information retrieval, scientific scenarios (chemistry, physics).
- Dataset selection: Based on the ability to demonstrate the specific skills and performance metrics of multi-agent systems in a well-rounded dataset.
- Comparison methods: Vanilla LLM, AutoAgents, Meta-prompting, a two-agent system, and existing baselines like GAIA_Orchestrator, FRIDAY, Warm-up Act, and HuggingFace Agent.
Benefits of Adaptive Build Versus Static Build
- Team Adaptability: Adaptive teams can adjust membership to better match the demands of each specific subtask in a continuously evolving manner, whereas static teams may lack this adaptability.
- Optimized Agent Selection: Adaptively creating teams to solve tasks using the respective strengths of various agents, whereas a static team may have redundant members or missing crucial skills.
- Dynamic Team Optimization: Adaptive teams can adapt and refine their composition and strategy during the task, whereas the expertise in fixed static teams can lead to suboptimal or inefficient processes.
- Reduced Redundancy: Adaptive teams are less prone to the problem of selecting redundant agents, allowing more significant specialization and preventing wasted efforts, whereas static builds can overload team members with unnecessary duties.
Related Works
- Large Language Models (LLMs): Include reasoning, planning, and adaptability.
- Multi-agent systems: Various approaches exist for forming teams (e.g., static, reactive, adaptive) and utilizing tools.
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Description
Explore the innovative adaptive team-building paradigm designed for language model agents. This approach highlights the dynamic formation of teams, managed by a 'Captain Agent' to enhance task execution. Learn how nested conversations and reflection aid in optimizing team expertise across diverse scenarios.