Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
Yuheng Cheng, Ceyao Zhang, Zhengwen Zhang, Xiangrui Meng, Sirui Hong,, Wenhao Li, Zihao Wang, Zekai Wang, Feng Yin, Junhua Zhao, Xiuqiang He

TL;DR
This paper provides a comprehensive survey of large language model-based intelligent agents, detailing their definitions, frameworks, components, deployment in multi-agent systems, and future prospects in AI research.
Contribution
It offers an in-depth overview of LLM-based agents, including their design, methods, and deployment strategies, highlighting recent advancements and future directions.
Findings
LLM-based agents demonstrate strong generalization across diverse applications.
Multi-agent systems utilize message passing and role collaboration to enhance performance.
The survey identifies key datasets and application scenarios for LLM-based agents.
Abstract
Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current research to provide an in-depth overview of LLM-based intelligent agents within single-agent and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques
