Summary of Llm-based Multi-agent Systems: Techniques and Business Perspectives, by Yingxuan Yang et al.
LLM-based Multi-Agent Systems: Techniques and Business Perspectives
by Yingxuan Yang, Qiuying Peng, Jun Wang, Ying Wen, Weinan Zhang
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper presents an innovative approach to large language models (LLMs), which can reformulate operational processes using LLM agents. These agents perceive, control, and receive feedback from their environment to accomplish tasks autonomously. They can also call external tools to ease task completion, which are considered predefined operational processes with private or real-time knowledge not contained in LLM parameters. As a natural trend, these tools become autonomous agents, forming a Large Language Model-based Multi-Agent System (LaMAS). LaMAS offers advantages such as dynamic task decomposition, higher flexibility, proprietary data preservation, and monetization feasibility for each entity. The paper discusses the technical and business landscapes of LaMAS, providing a preliminary protocol considering technical requirements, data privacy, and business incentives to support the ecosystem. As such, LaMAS can be a practical solution to achieve artificial collective intelligence in the near future. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LaMAS is an exciting new approach that uses large language models to help machines work together more effectively. Right now, big computers can perform many tasks on their own using these language models. But what if we could make them work together even better? That’s where LaMAS comes in. It lets different machines talk to each other and share information to get the job done more efficiently. This is important because it means that machines can learn from each other and become even smarter over time. |
Keywords
» Artificial intelligence » Large language model
