A Comprehensive Overview of LLM-Based Autonomous Agents

Introduction

The research paper "A Survey on Large Language Model based Autonomous Agents" from Renmin University of China presents a detailed overview of the advancements in the field of autonomous agents driven by Large Language Models (LLMs). This paper provides insights into various aspects of agent architecture, including profiling, memory, planning, and action modules, along with their applications, evaluation strategies, and future directions.

Profiling Module

Overview

The profiling module shapes the identity and personality of the autonomous agent, influencing its interactions and responses.

Methods

1. Handcraft Method: Manually designing the agent's profile.
2. LLM Generation Method: Generating profiles using AI, leveraging the capabilities of LLMs.
3. Dataset Alignment Method: Aligning the agent's profile with specific datasets to reflect targeted domains or demographics.

Memory Module

Overview

The memory module stores information from the environment and leverages past experiences to facilitate future actions.

Structures

1. Unified Memory: Simulating short-term human memory, realized by in-context learning.
2. Hybrid Memory: Modeling both short-term and long-term human memories.

Formats

1. Natural Languages: Describing memory information using raw natural language.
2. Embeddings: Encoding memory information into embedding vectors.
3. Databases: Storing memory information in databases.
4. Structured Lists: Organizing memory information into lists.

Operations

1. Memory Reading: Extracting valuable information from memory.
2. Memory Writing: Storing environmental information in memory.
3. Memory Reflection: Summarizing and inferring abstract, complex high-level information.

Planning Module

Overview

The planning module enables agents to deconstruct complex tasks into simpler subtasks.

Strategies

1. Planning without Feedback: Including single-path and multi-path reasoning, and using external planners.
2. Planning with Feedback: Incorporating environmental, human, and model feedback to adapt plans.

Action Module

Overview

The action module translates the agent’s decisions into specific outcomes.

Aspects

1. Action Goal: Performing actions with various objectives like task completion, communication, and environment exploration.
2. Action Production: Generating actions via memory recollection or following pre-generated plans.
3. Action Space: Includes external tools (APIs, databases, external models) and internal knowledge of LLMs.
4. Action Impact: Refers to the consequences of actions, such as changing environments and altering the agent's internal states.

Applications and Evaluation

• Diverse applications in social science, natural science, and engineering.
• Evaluation strategies include both subjective and objective measures.

Challenges and Future Directions

• Addressing limitations like prompt robustness and hallucination issues.
• Ethical considerations and social impact.
• Future research directions include improving human alignment and role-playing capabilities.

Conclusion

The survey by Lei Wang and colleagues offers a comprehensive view of LLM-based autonomous agents, highlighting their potential and the complexities involved in their development. It underscores the importance of each module in creating effective autonomous agents and points out the vast applications and challenges in this field.

References

• A Survey on Large Language Model based Autonomous Agents. Link to paper

Related

• paper Multi-Agent Reasoning with Large Language Models for Effective Corporate Planning - 2024-01-03
• paper Cognitive Architectures for Language Agents - 2024-04-01
• paper Training Language Model Agents without Modifying Language Models - 2024-03-19
• paper A Survey on Language Models for Code: from Statistical Models to AI-driven Code Mastery - 2023-11-28
• paper Self-Discover: Large Language Models Self-Compose Reasoning Structures - 2024-02-25
• paper AutoGen: Unleashing the Power of Multi-Agent Conversations in LLM Applications - 2023-11-12
• paper Retrieval-Augmented Generation for Large Language Models: A Survey - 2024-03-31
• paper Diffusion Models for Reinforcement Learning: A Survey - 2023-12-13
• paper Voyager: An Open-Ended Embodied Agent with Large Language Models - 2024-04-13
• paper Unraveling EcoAssistant: Autogen's Advancement in Economical and Precise Code-Driven Question Answering - 2023-11-13

Created 2023-11-10T18:15:15-08:00, updated 2023-12-09T08:30:54-08:00 · History · Edit