Summary of Large Language Model Based Multi-agents: a Survey Of Progress and Challenges, by Taicheng Guo et al.
Large Language Model based Multi-Agents: A Survey of Progress and Challenges
by Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have achieved remarkable success across various tasks due to their impressive planning and reasoning abilities. Recently, LLM-based multi-agent systems have made significant progress in complex problem-solving and world simulation. This survey aims to provide an in-depth discussion on the essential aspects of LLM-based multi-agent systems, including challenges, domains, environments, profiling, communication mechanisms, capacity growth, and commonly used datasets or benchmarks. The goal is to offer readers substantial insights into this dynamic field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart machines that can do many things on their own. They’re like superheroes! Recently, people have been combining multiple LLMs to create teams that work together to solve really hard problems and simulate complex worlds. This paper is a big review of this field, covering what these teams do, how they work, and what challenges they face. It’s like a guidebook for anyone interested in learning more about this exciting area of research. |
