Adaptive Team Building for Language Models

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Questions and Answers

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?

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?

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?

Retrieval-Augmented Generation (RAG) Signup and view all the answers

What type of agents does Captain Agent retrieve based on the role description?

Agents that have top-k1 relevance to the role descriptions Signup and view all the answers

What is the primary responsibility of the User Proxy Agent?

To execute code and potentially terminate conversations Signup and view all the answers

Which backbone is used by the HuggingFace Agent?

LLaMA-3-70B Signup and view all the answers

What dataset is mentioned in relation to initializing the agent library?

A small subset of problem instances from each dataset Signup and view all the answers

How is fairness in evaluation ensured for the methods being compared?

By transforming different result formats to a uniform format Signup and view all the answers

Which of the following categories is NOT included in the tool library?

Data Mining Signup and view all the answers

What happens to the agent library during the main experiment?

It remains unchanged while updated agents are added Signup and view all the answers

What is the primary purpose of the callable Python functions in the tool library?

To enable freeform coding to address sophisticated tasks Signup and view all the answers

What is the required default response from the User Proxy Agent when a problem is deemed solved?

TERMINATE Signup and view all the answers

Which method achieved the highest average accuracy across different scenarios?

Captain Agent Signup and view all the answers

What is the average accuracy of the AutoGen method in the real-world scenarios?

79.89 Signup and view all the answers

In the world-information retrieval scenario, which method displayed the lowest performance at Level 3?

Huggingface-Agent (LLaMA-3-70B) Signup and view all the answers

Which of the following methods had a lower accuracy in Mathematics compared to Captain Agent?

All of the above Signup and view all the answers

What was the accuracy of the Warm-up Act at Level 1 in the world-information retrieval scenario?

35.19 Signup and view all the answers

Which method had the highest accuracy in Programming tasks?

Captain Agent Signup and view all the answers

What unique advantage does Captain Agent provide compared to other methods in the context of accuracy?

Minimal prompt engineering requirements Signup and view all the answers

What is the achievement of the Captain Agent in relation to the success token method described?

It produces a unique token if code passes all tests. Signup and view all the answers

Which method showed the highest average accuracy across the three levels in the world-information retrieval scenario?

Both Agent and Tool Library Signup and view all the answers

In the comparison of different LLM backbones, which backbone achieved the highest accuracy in Mathematics?

gpt-4-0125-preview Signup and view all the answers

Which selection resulted in the lowest accuracy in Data Analysis?

Mixtral-8x22B-instruct-v0.1 Signup and view all the answers

What was the average accuracy of the Adaptive Team (Captain Agent) across all subjects?

79.20 Signup and view all the answers

Which model reported an accuracy of 39.62 in Chemistry using the Tool Library?

Captain Agent with Tool Library Signup and view all the answers

Which backbone achieved the lowest accuracy in Physics?

Mixtral-8x22B-instruct-v0.1 Signup and view all the answers

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?

Programming Signup and view all the answers

What was the average accuracy of the Static Team across all subjects?

65.68 Signup and view all the answers

What is the main task regarding the BBC Earth video titled 'Top 5 Silliest Animal Moments'?

To identify the species of bird featured Signup and view all the answers

Who is responsible for verifying the accuracy of the bird identification?

The Fact Checker Signup and view all the answers

What is one of the first steps that the Digital Media Analyst takes in the task?

To search for the BBC Earth video Signup and view all the answers

Which expert is primarily tasked with watching the video for bird identification?

The Zoologist Signup and view all the answers

What is the expected output after completing the task related to the video?

The name of the bird species featured Signup and view all the answers

What is the primary focus of the paper 'Iterative forward tuning boosts in-context learning in language models'?

Improving in-context learning through iterative tuning Signup and view all the answers

What concept is discussed in 'Why can GPT learn in-context?'?

The capability of GPT to perform gradient descent as a meta-optimizer Signup and view all the answers

What do the authors of 'Transformers as algorithms' suggest about generalization in in-context learning?

Stability in learning contributes to better generalization Signup and view all the answers

What innovative approach does 'Adaplanner' present in relation to language models?

Adaptive planning that utilizes feedback effectively Signup and view all the answers

Which advancement is highlighted in 'Chain of thought prompting elicits reasoning in large language models'?

The method of enhancing reasoning through prompt design Signup and view all the answers

What is the contribution of 'Toolformer' in relation to language models?

Training language models to utilize tools autonomously Signup and view all the answers

The research presented in 'Large language models as tool makers' mainly focuses on what aspect of language models?

The ability of language models to generate tools Signup and view all the answers

What does 'Travelplanner' aim to evaluate in the context of language models?

Real-world planning capabilities of language agents Signup and view all the answers

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.

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.