Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- URL: http://arxiv.org/abs/2402.01680v2
- Date: Fri, 19 Apr 2024 01:15:16 GMT
- Title: Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- Authors: Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang,
- Abstract summary: Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks.
Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation.
- Score: 44.92286030322281
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation. To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges. Our goal is for readers to gain substantial insights on the following questions: What domains and environments do LLM-based multi-agents simulate? How are these agents profiled and how do they communicate? What mechanisms contribute to the growth of agents' capacities? For those interested in delving into this field of study, we also summarize the commonly used datasets or benchmarks for them to have convenient access. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository, dedicated to outlining the research on LLM-based multi-agent systems.
Related papers
- Agents in Software Engineering: Survey, Landscape, and Vision [46.021478509599895]
Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks.
We find that many studies combining LLMs with software engineering (SE) have employed the concept of agents either explicitly or implicitly.
We present a framework of LLM-based agents in SE which includes three key modules: perception, memory, and action.
arXiv Detail & Related papers (2024-09-13T17:55:58Z) - Large Language Model-Based Agents for Software Engineering: A Survey [20.258244647363544]
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents.
We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives.
In addition, we discuss open challenges and future directions in this critical domain.
arXiv Detail & Related papers (2024-09-04T15:59:41Z) - LLMs Meet Multimodal Generation and Editing: A Survey [89.76691959033323]
This survey elaborates on multimodal generation and editing across various domains, comprising image, video, 3D, and audio.
We summarize the notable advancements with milestone works in these fields and categorize these studies into LLM-based and CLIP/T5-based methods.
We dig into tool-augmented multimodal agents that can leverage existing generative models for human-computer interaction.
arXiv Detail & Related papers (2024-05-29T17:59:20Z) - LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions [8.55917897789612]
We focus on the cooperative tasks of multiple agents with a common goal and communication among them.
We also consider human-in/on-the-loop scenarios enabled by the language component in the framework.
arXiv Detail & Related papers (2024-05-17T22:10:23Z) - A Survey on the Memory Mechanism of Large Language Model based Agents [66.4963345269611]
Large language model (LLM) based agents have recently attracted much attention from the research and industry communities.
LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems.
The key component to support agent-environment interactions is the memory of the agents.
arXiv Detail & Related papers (2024-04-21T01:49:46Z) - Large Multimodal Agents: A Survey [78.81459893884737]
Large language models (LLMs) have achieved superior performance in powering text-based AI agents.
There is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain.
This review aims to provide valuable insights and guidelines for future research in this rapidly evolving field.
arXiv Detail & Related papers (2024-02-23T06:04:23Z) - Exploring Large Language Model based Intelligent Agents: Definitions,
Methods, and Prospects [32.91556128291915]
This paper surveys current research to provide an in-depth overview of intelligent agents within single 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 feedback.
We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.
arXiv Detail & Related papers (2024-01-07T09:08:24Z) - A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence.
This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
arXiv Detail & Related papers (2023-08-22T13:30:37Z) - AgentBench: Evaluating LLMs as Agents [88.45506148281379]
Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks.
We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
arXiv Detail & Related papers (2023-08-07T16:08:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.