A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components

Xinzhe Li·June 09, 2024

Summary

This survey explores advancements in Large Language Models (LLMs) for agent development, focusing on simplifying frameworks and identifying reusable components (LLMPCs). It categorizes workflows into interaction environments (tool-use, search, feedback-learning) and discusses eight common workflows, such as policy-only, search, and feedback-learning, in gaming, embodied, NLIE, and tool environments. LLMs are integrated for decision-making, problem-solving, and tool integration, with a taxonomy of frameworks and task-specific LMPCs like glmpolicy, glmeval, and glmdynamic. The study highlights the adaptability of these models for various tasks and the role of few-shot and zero-shot learning, while also addressing issues like model bias and resource management.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the complexity of frameworks for developing Large Language Model (LLM)-based agents, focusing on nuanced differentiation at a granular level to enable efficient implementations across different frameworks and foster future research . This survey seeks to facilitate a cohesive understanding of diverse frameworks by identifying common workflows and reusable LLM-Profiled Components (LMPCs) . The primary goal is to provide insights into the commonalities among recently proposed frameworks for LLM-based agents, emphasizing the importance of understanding these components for effective implementation and advancement in research . This is not a new problem but rather a critical aspect in the development and utilization of LLM-based agents, highlighting the need for a comprehensive survey to navigate the complexities of these frameworks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that recent advancements in Large Language Models (LLMs) have led to the development of sophisticated frameworks for creating LLM-based agents, which can actively interact with external tools and environments, functioning as integral components of agency, including acting, planning, and evaluating . The primary focus is on identifying common workflows and reusable LLM-Profiled Components (LMPCs) to enable efficient implementations across different frameworks and foster future research in this area .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components" proposes several new ideas, methods, and models in the field of Large Language Models (LLMs) and their applications in developing sophisticated frameworks for LLM-based agents . Here are some key contributions and proposals from the paper:

  1. Common Workflows and Reusable LLM-Profiled Components (LMPCs): The paper aims to facilitate a cohesive understanding of diverse frameworks by identifying common workflows and reusable LMPCs across various LLM-based agents . It categorizes and details different workflows such as tool-use workflows, search workflows, and feedback-learning workflows, which are essential components in LLM-based agent implementations .

  2. Task Environments and Tool Environments: The paper explores task environments and tool environments specific to LLM-based agents, which differ from traditional AI and reinforcement learning frameworks . It discusses the involvement of LLMs within agentic workflows and clarifies their roles in agent implementations, focusing on creating common workflows with reusable LMPCs .

  3. Prompting Methods and Strategies: The paper introduces prompting methods for LLM-Profiled Components (LMPCs) such as Few-shot ReAct, Reflexion, RAP, and MultiTool-CoT, which elicit reasoning and acting in large language models . It emphasizes the importance of distinguishing strategies for tool actions and task-specific actions within the ReAct framework .

  4. Feedback Types and Task Formulations: The paper discusses different feedback types and task formulations required for various workflows in LLM-based agents . It categorizes scenarios for generating free-form reflection, binary/multiclass classification, and binary classification with scalar values, highlighting the importance of feedback signals in guiding decision-making and search workflows .

In summary, the paper provides insights into the development of LLM-based agents by proposing common workflows, reusable LMPCs, prompting methods, and strategies for effective implementation and utilization of Large Language Models in various agent frameworks . The paper "A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components" introduces innovative characteristics and advantages compared to previous methods in the field of Large Language Models (LLMs) and their applications in developing LLM-based agents . Here are some key points highlighting these characteristics and advantages:

  1. Efficient Guided Generation: The paper proposes investigating constrained generation techniques as an approach to improve efficiency in LLM-based workflows . By utilizing such techniques, the generation of multiple potential nodes simultaneously in one step is enabled, enhancing the efficiency of search-based workflows compared to expanding nodes individually .

  2. Memory Management: The paper discusses the implementation of memory in LLM-based workflows, distinguishing between static and dynamic information handling . While static information like profiling messages is manually constructed and stored, dynamic information such as feedback is managed via runtime data structures during interactions within each workflow .

  3. Tool Environments Integration: Modern LLM agents are often enhanced with external tools to improve problem-solving capabilities . The design and integration of these tools add complexity, requiring careful consideration of how LLMs interact with both task environments and auxiliary tools, enhancing the agents' capabilities in problem-solving tasks .

  4. Feedback-Learning Workflows: The paper explores different sources of feedback, including glmeval, humans, task environments, and tools, in feedback-learning workflows . By utilizing feedback mechanisms like glmeval for reflection and learning, LLM-based agents can enhance their decision-making processes and improve performance in various workflows .

  5. Prompting Methods: The paper introduces prompting methods such as Few-shot ReAct, Reflexion, RAP, and MultiTool-CoT, which play a crucial role in eliciting reasoning and acting in large language models . These prompting methods provide structured approaches for generating responses and actions, contributing to the overall effectiveness of LLM-based agents in different workflows .

In summary, the paper's contributions in exploring efficient generation techniques, memory management, tool environments integration, feedback-learning workflows, and prompting methods offer significant advancements in the development and utilization of LLM-based agents, enhancing their capabilities and performance across various tasks and workflows .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of Large Language Models (LLMs) and language agents, there are several noteworthy researchers and related researches:

  • Some of the notable researchers in this field include Hirokazu Inaba, Fei Cheng, Sadao Kurohashi, Takeshi Kojima, Yutaka Matsuo, Stuart J. Russell, Percy Liang, and many others .
  • The key to the solution mentioned in the paper involves a structured plan to solve a given problem step by step. This includes calculating the total number of students enrolled in different types of dances, determining the remaining students, and calculating the percentage of students enrolled in a specific dance style .

How were the experiments in the paper designed?

The experiments in the paper were designed to explore various aspects related to Large Language Models (LLMs) and LLM-based agents. The experiments focused on:

  • Investigating constrained generation techniques for efficient rewards and action selection .
  • Implementing memory systems for handling static and dynamic information during interactions within workflows .
  • Profiling LLMs using Chain-of-Thought (CoT) prompting to enhance reasoning capabilities and intermediate steps creation .
  • Implementing different strategies for tool use, feedback learning, and task-specific actions within workflows .
  • Examining workflow-specific LLM-Profiled Evaluators based on task formulation and feedback types .
  • Creating a task-agnostic tool environment to encompass a wide array of tools suitable for various tasks .
  • Exploring stochastic glmactor to enhance the stochastic nature of glmpolicy and improve efficiency .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context is the GSM8K dataset . The code for the dataset is not explicitly mentioned as open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results in the paper provide strong support for the scientific hypotheses that need to be verified. The paper discusses the implementation of Large Language Models (LLMs) in various workflows and the utilization of LLM-Profiled Components (LMPCs) . These components are designed to enhance the capabilities of LLM-based agents in tasks such as acting, planning, and evaluating . The experiments detailed in the paper demonstrate the effectiveness of different workflows and LMPCs in enabling LLM-based agents to interact actively with external tools and environments .

Furthermore, the paper highlights the use of Chain-of-Thought (CoT) prompting techniques, such as Zero-shot CoT and few-shot CoT, to enhance the reasoning capabilities of LLMs . These prompting techniques facilitate intermediate reasoning steps and improve the overall performance of LLM-based agents in various tasks . The experiments conducted in the paper showcase how these CoT prompting strategies contribute to the successful implementation of LLM policy models .

Moreover, the paper discusses the integration of search algorithms, tree structures, and Reinforcement Learning (RL) components in the workflows of LLM-based agents . These components play a crucial role in enhancing the decision-making and problem-solving abilities of LLM-based agents . The experiments conducted in the paper provide empirical evidence of how these components contribute to the overall performance and efficiency of LLM-based agents in different scenarios .

In conclusion, the experiments and results presented in the paper offer substantial support for the scientific hypotheses related to the effective utilization of LLMs, CoT prompting techniques, and various components in enhancing the capabilities of LLM-based agents across different workflows and tasks . The findings demonstrate the feasibility and effectiveness of these approaches in improving the performance and functionality of LLM-based agents in diverse settings.


What are the contributions of this paper?

The paper on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components offers several key contributions:

  1. Simplifying Complex Frameworks: The survey aims to simplify the understanding of intricate frameworks by breaking them down into implementable workflows, particularly tailored for specific tasks. It emphasizes the identification of reusable workflows and LLM-Profiled Components (LMPCs) across popular frameworks like ReAct, Reflexion, and Tree-of-Thoughts .
  2. Enhancing Assessment of Frameworks: It helps researchers and practitioners assess current frameworks at a more detailed and cohesive level by categorizing prominent frameworks and showcasing how they are constructed through common workflows and LMPCs .
  3. Facilitating Framework Extensions: The survey facilitates the extension of existing frameworks by detailing the implementations of LMPCs and their applicability across various workflows and tasks, enabling modifications to enhance functionality .

What work can be continued in depth?

The work that can be continued in depth involves the exploration of planning or search algorithms that enable sequential decisions to be organized into a tree or graph for exploration . These algorithms can facilitate strategic searches over actions derived from multiple reasoning paths, utilizing techniques such as beam search, depth-first and breadth-first search, and Monte-Carlo Tree Search . Additionally, the utilization of LMPCs, such as glmpolicy for action sampling and glmeval for value calculation during exploration, can be further studied to enhance decision-making processes in LLM-based agents .


Introduction
Background
Evolution of LLMs in agent development
Importance of simplification and reusability in frameworks
Objective
To analyze LLM advancements
Identify reusable components (LLMPCs)
Categorize workflows and environments
Methodology
Data Collection
Review of recent LLM literature
Case studies and empirical evaluations
Data Preprocessing
Selection criteria for frameworks and LLMPCs
Analysis of model architectures and performance
Workflow Categorization
Interaction Environments
Tool-Use
Policy-only approaches
Integration with tools
Search
Heuristic-based search
LLM-driven exploration
Feedback-Learning
Reinforcement learning with LLM feedback
Adaptive learning through LLMs
Gaming and Embodied Agents
Game-specific LLM applications
Physical environment interactions
NLIE (Natural Language Interaction Environments)
Language understanding and generation
Conversational agents
Task-Specific Environments
glmpolicy, glmeval, glmdynamic taxonomy
LLM Integration and Decision-Making
LLMs for problem-solving
Decision-making strategies
Role of few-shot and zero-shot learning
Model Bias and Fairness
Addressing biases in LLMs
Mitigation techniques
Ethical considerations
Resource Management
Energy efficiency in LLM deployment
Scalability and computational requirements
Memory optimization strategies
Conclusion
Summary of key findings
Future directions for LLM research in agent development
Implications for industry and real-world applications
Basic info
papers
computation and language
software engineering
artificial intelligence
Advanced features
Insights
What is the primary focus of the survey on Large Language Models (LLMs) for agent development?
How do the workflows in the survey categorize interaction environments for LLMs?
What are the main applications of LLMs in the survey, such as decision-making and problem-solving, and which specific LLMPCs are mentioned?
What are some examples of common workflows discussed in the gaming, embodied, NLIE, and tool environments?

A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components

Xinzhe Li·June 09, 2024

Summary

This survey explores advancements in Large Language Models (LLMs) for agent development, focusing on simplifying frameworks and identifying reusable components (LLMPCs). It categorizes workflows into interaction environments (tool-use, search, feedback-learning) and discusses eight common workflows, such as policy-only, search, and feedback-learning, in gaming, embodied, NLIE, and tool environments. LLMs are integrated for decision-making, problem-solving, and tool integration, with a taxonomy of frameworks and task-specific LMPCs like glmpolicy, glmeval, and glmdynamic. The study highlights the adaptability of these models for various tasks and the role of few-shot and zero-shot learning, while also addressing issues like model bias and resource management.
Mind map
Conversational agents
Language understanding and generation
Physical environment interactions
Game-specific LLM applications
Adaptive learning through LLMs
Reinforcement learning with LLM feedback
LLM-driven exploration
Heuristic-based search
Integration with tools
Policy-only approaches
Memory optimization strategies
Scalability and computational requirements
Energy efficiency in LLM deployment
Ethical considerations
Mitigation techniques
Addressing biases in LLMs
glmpolicy, glmeval, glmdynamic taxonomy
Task-Specific Environments
NLIE (Natural Language Interaction Environments)
Gaming and Embodied Agents
Feedback-Learning
Search
Tool-Use
Analysis of model architectures and performance
Selection criteria for frameworks and LLMPCs
Case studies and empirical evaluations
Review of recent LLM literature
Categorize workflows and environments
Identify reusable components (LLMPCs)
To analyze LLM advancements
Importance of simplification and reusability in frameworks
Evolution of LLMs in agent development
Implications for industry and real-world applications
Future directions for LLM research in agent development
Summary of key findings
Resource Management
Model Bias and Fairness
Interaction Environments
Data Preprocessing
Data Collection
Objective
Background
Conclusion
LLM Integration and Decision-Making
Workflow Categorization
Methodology
Introduction
Outline
Introduction
Background
Evolution of LLMs in agent development
Importance of simplification and reusability in frameworks
Objective
To analyze LLM advancements
Identify reusable components (LLMPCs)
Categorize workflows and environments
Methodology
Data Collection
Review of recent LLM literature
Case studies and empirical evaluations
Data Preprocessing
Selection criteria for frameworks and LLMPCs
Analysis of model architectures and performance
Workflow Categorization
Interaction Environments
Tool-Use
Policy-only approaches
Integration with tools
Search
Heuristic-based search
LLM-driven exploration
Feedback-Learning
Reinforcement learning with LLM feedback
Adaptive learning through LLMs
Gaming and Embodied Agents
Game-specific LLM applications
Physical environment interactions
NLIE (Natural Language Interaction Environments)
Language understanding and generation
Conversational agents
Task-Specific Environments
glmpolicy, glmeval, glmdynamic taxonomy
LLM Integration and Decision-Making
LLMs for problem-solving
Decision-making strategies
Role of few-shot and zero-shot learning
Model Bias and Fairness
Addressing biases in LLMs
Mitigation techniques
Ethical considerations
Resource Management
Energy efficiency in LLM deployment
Scalability and computational requirements
Memory optimization strategies
Conclusion
Summary of key findings
Future directions for LLM research in agent development
Implications for industry and real-world applications
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the complexity of frameworks for developing Large Language Model (LLM)-based agents, focusing on nuanced differentiation at a granular level to enable efficient implementations across different frameworks and foster future research . This survey seeks to facilitate a cohesive understanding of diverse frameworks by identifying common workflows and reusable LLM-Profiled Components (LMPCs) . The primary goal is to provide insights into the commonalities among recently proposed frameworks for LLM-based agents, emphasizing the importance of understanding these components for effective implementation and advancement in research . This is not a new problem but rather a critical aspect in the development and utilization of LLM-based agents, highlighting the need for a comprehensive survey to navigate the complexities of these frameworks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that recent advancements in Large Language Models (LLMs) have led to the development of sophisticated frameworks for creating LLM-based agents, which can actively interact with external tools and environments, functioning as integral components of agency, including acting, planning, and evaluating . The primary focus is on identifying common workflows and reusable LLM-Profiled Components (LMPCs) to enable efficient implementations across different frameworks and foster future research in this area .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components" proposes several new ideas, methods, and models in the field of Large Language Models (LLMs) and their applications in developing sophisticated frameworks for LLM-based agents . Here are some key contributions and proposals from the paper:

  1. Common Workflows and Reusable LLM-Profiled Components (LMPCs): The paper aims to facilitate a cohesive understanding of diverse frameworks by identifying common workflows and reusable LMPCs across various LLM-based agents . It categorizes and details different workflows such as tool-use workflows, search workflows, and feedback-learning workflows, which are essential components in LLM-based agent implementations .

  2. Task Environments and Tool Environments: The paper explores task environments and tool environments specific to LLM-based agents, which differ from traditional AI and reinforcement learning frameworks . It discusses the involvement of LLMs within agentic workflows and clarifies their roles in agent implementations, focusing on creating common workflows with reusable LMPCs .

  3. Prompting Methods and Strategies: The paper introduces prompting methods for LLM-Profiled Components (LMPCs) such as Few-shot ReAct, Reflexion, RAP, and MultiTool-CoT, which elicit reasoning and acting in large language models . It emphasizes the importance of distinguishing strategies for tool actions and task-specific actions within the ReAct framework .

  4. Feedback Types and Task Formulations: The paper discusses different feedback types and task formulations required for various workflows in LLM-based agents . It categorizes scenarios for generating free-form reflection, binary/multiclass classification, and binary classification with scalar values, highlighting the importance of feedback signals in guiding decision-making and search workflows .

In summary, the paper provides insights into the development of LLM-based agents by proposing common workflows, reusable LMPCs, prompting methods, and strategies for effective implementation and utilization of Large Language Models in various agent frameworks . The paper "A Survey on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components" introduces innovative characteristics and advantages compared to previous methods in the field of Large Language Models (LLMs) and their applications in developing LLM-based agents . Here are some key points highlighting these characteristics and advantages:

  1. Efficient Guided Generation: The paper proposes investigating constrained generation techniques as an approach to improve efficiency in LLM-based workflows . By utilizing such techniques, the generation of multiple potential nodes simultaneously in one step is enabled, enhancing the efficiency of search-based workflows compared to expanding nodes individually .

  2. Memory Management: The paper discusses the implementation of memory in LLM-based workflows, distinguishing between static and dynamic information handling . While static information like profiling messages is manually constructed and stored, dynamic information such as feedback is managed via runtime data structures during interactions within each workflow .

  3. Tool Environments Integration: Modern LLM agents are often enhanced with external tools to improve problem-solving capabilities . The design and integration of these tools add complexity, requiring careful consideration of how LLMs interact with both task environments and auxiliary tools, enhancing the agents' capabilities in problem-solving tasks .

  4. Feedback-Learning Workflows: The paper explores different sources of feedback, including glmeval, humans, task environments, and tools, in feedback-learning workflows . By utilizing feedback mechanisms like glmeval for reflection and learning, LLM-based agents can enhance their decision-making processes and improve performance in various workflows .

  5. Prompting Methods: The paper introduces prompting methods such as Few-shot ReAct, Reflexion, RAP, and MultiTool-CoT, which play a crucial role in eliciting reasoning and acting in large language models . These prompting methods provide structured approaches for generating responses and actions, contributing to the overall effectiveness of LLM-based agents in different workflows .

In summary, the paper's contributions in exploring efficient generation techniques, memory management, tool environments integration, feedback-learning workflows, and prompting methods offer significant advancements in the development and utilization of LLM-based agents, enhancing their capabilities and performance across various tasks and workflows .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of Large Language Models (LLMs) and language agents, there are several noteworthy researchers and related researches:

  • Some of the notable researchers in this field include Hirokazu Inaba, Fei Cheng, Sadao Kurohashi, Takeshi Kojima, Yutaka Matsuo, Stuart J. Russell, Percy Liang, and many others .
  • The key to the solution mentioned in the paper involves a structured plan to solve a given problem step by step. This includes calculating the total number of students enrolled in different types of dances, determining the remaining students, and calculating the percentage of students enrolled in a specific dance style .

How were the experiments in the paper designed?

The experiments in the paper were designed to explore various aspects related to Large Language Models (LLMs) and LLM-based agents. The experiments focused on:

  • Investigating constrained generation techniques for efficient rewards and action selection .
  • Implementing memory systems for handling static and dynamic information during interactions within workflows .
  • Profiling LLMs using Chain-of-Thought (CoT) prompting to enhance reasoning capabilities and intermediate steps creation .
  • Implementing different strategies for tool use, feedback learning, and task-specific actions within workflows .
  • Examining workflow-specific LLM-Profiled Evaluators based on task formulation and feedback types .
  • Creating a task-agnostic tool environment to encompass a wide array of tools suitable for various tasks .
  • Exploring stochastic glmactor to enhance the stochastic nature of glmpolicy and improve efficiency .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context is the GSM8K dataset . The code for the dataset is not explicitly mentioned as open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results in the paper provide strong support for the scientific hypotheses that need to be verified. The paper discusses the implementation of Large Language Models (LLMs) in various workflows and the utilization of LLM-Profiled Components (LMPCs) . These components are designed to enhance the capabilities of LLM-based agents in tasks such as acting, planning, and evaluating . The experiments detailed in the paper demonstrate the effectiveness of different workflows and LMPCs in enabling LLM-based agents to interact actively with external tools and environments .

Furthermore, the paper highlights the use of Chain-of-Thought (CoT) prompting techniques, such as Zero-shot CoT and few-shot CoT, to enhance the reasoning capabilities of LLMs . These prompting techniques facilitate intermediate reasoning steps and improve the overall performance of LLM-based agents in various tasks . The experiments conducted in the paper showcase how these CoT prompting strategies contribute to the successful implementation of LLM policy models .

Moreover, the paper discusses the integration of search algorithms, tree structures, and Reinforcement Learning (RL) components in the workflows of LLM-based agents . These components play a crucial role in enhancing the decision-making and problem-solving abilities of LLM-based agents . The experiments conducted in the paper provide empirical evidence of how these components contribute to the overall performance and efficiency of LLM-based agents in different scenarios .

In conclusion, the experiments and results presented in the paper offer substantial support for the scientific hypotheses related to the effective utilization of LLMs, CoT prompting techniques, and various components in enhancing the capabilities of LLM-based agents across different workflows and tasks . The findings demonstrate the feasibility and effectiveness of these approaches in improving the performance and functionality of LLM-based agents in diverse settings.


What are the contributions of this paper?

The paper on LLM-Based Agents: Common Workflows and Reusable LLM-Profiled Components offers several key contributions:

  1. Simplifying Complex Frameworks: The survey aims to simplify the understanding of intricate frameworks by breaking them down into implementable workflows, particularly tailored for specific tasks. It emphasizes the identification of reusable workflows and LLM-Profiled Components (LMPCs) across popular frameworks like ReAct, Reflexion, and Tree-of-Thoughts .
  2. Enhancing Assessment of Frameworks: It helps researchers and practitioners assess current frameworks at a more detailed and cohesive level by categorizing prominent frameworks and showcasing how they are constructed through common workflows and LMPCs .
  3. Facilitating Framework Extensions: The survey facilitates the extension of existing frameworks by detailing the implementations of LMPCs and their applicability across various workflows and tasks, enabling modifications to enhance functionality .

What work can be continued in depth?

The work that can be continued in depth involves the exploration of planning or search algorithms that enable sequential decisions to be organized into a tree or graph for exploration . These algorithms can facilitate strategic searches over actions derived from multiple reasoning paths, utilizing techniques such as beam search, depth-first and breadth-first search, and Monte-Carlo Tree Search . Additionally, the utilization of LMPCs, such as glmpolicy for action sampling and glmeval for value calculation during exploration, can be further studied to enhance decision-making processes in LLM-based agents .

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