Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Large Language Model based Multi-Agents: A Survey of Progress and Challenges

19 Apr 2024 | Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
This paper provides a comprehensive survey of the progress and challenges in Large Language Model (LLM)-based multi-agent systems. LLMs have demonstrated remarkable capabilities in planning and reasoning, making them suitable for autonomous agent applications. The survey highlights the development of LLM-based multi-agent systems, which have shown significant progress in complex problem-solving and world simulation. Key aspects discussed include the agents-environment interface, agent profiling, communication, and capability acquisition. The paper also categorizes current applications into problem-solving and world simulation, and provides an overview of open-source implementation frameworks, datasets, and benchmarks. Challenges addressed include multi-modal environments, hallucination, collective intelligence, and scalability. The authors aim to provide insights into the fundamental concepts and latest research trends in LLM-based multi-agent systems, fostering further exploration and innovation in this dynamic field.This paper provides a comprehensive survey of the progress and challenges in Large Language Model (LLM)-based multi-agent systems. LLMs have demonstrated remarkable capabilities in planning and reasoning, making them suitable for autonomous agent applications. The survey highlights the development of LLM-based multi-agent systems, which have shown significant progress in complex problem-solving and world simulation. Key aspects discussed include the agents-environment interface, agent profiling, communication, and capability acquisition. The paper also categorizes current applications into problem-solving and world simulation, and provides an overview of open-source implementation frameworks, datasets, and benchmarks. Challenges addressed include multi-modal environments, hallucination, collective intelligence, and scalability. The authors aim to provide insights into the fundamental concepts and latest research trends in LLM-based multi-agent systems, fostering further exploration and innovation in this dynamic field.
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