Summary of Scaling Large Language Model-based Multi-agent Collaboration, by Chen Qian et al.
Scaling Large Language Model-based Multi-Agent Collaboration
by Chen Qian, Zihao Xie, YiFei Wang, Wei Liu, Kunlun Zhu, Hanchen Xia, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI); Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
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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 The paper investigates whether the continuous addition of collaborative agents can improve performance, inspired by the neural scaling law. It proposes a multi-agent collaboration network (MacNet) and evaluates its effectiveness in supporting collaboration among over a thousand agents. The results show that irregular topologies outperform regular ones, and the overall performance follows a logistic growth pattern as agents scale. This may be due to the catalyzing effect of agent scaling on multidimensional considerations during interactive reflection and refinement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how many “smart” computers working together can solve problems better than one computer alone. They create a special network for these smart computers to work together, called MacNet. When they test it with thousands of computers, the weirdly arranged networks do even better than the ones that are all lined up. It’s like when you get more people working on a project together – sometimes things come together in a really cool way! The code is available online. |
