Summary of On the Resilience Of Llm-based Multi-agent Collaboration with Faulty Agents, by Jen-tse Huang et al.
On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents
by Jen-tse Huang, Jiaxu Zhou, Tailin Jin, Xuhui Zhou, Zixi Chen, Wenxuan Wang, Youliang Yuan, Michael R. Lyu, Maarten Sap
First submitted to arxiv on: 2 Aug 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 The paper investigates the impact of faulty agents on large language model-based multi-agent systems and proposes methods to increase system resilience. The study explores different system structures, including A→B→C and A←B←C, and evaluates their performance under faulty agents in various downstream tasks such as code generation, math problems, translation, and text evaluation. The results show that the hierarchical structure exhibits superior resilience, with a performance drop of 9.2%, compared to other structures. To improve system resilience, two methods are proposed: introducing a mechanism for each agent to challenge others’ outputs and adding an additional agent to review and correct messages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large teams of AI agents work together when some agents make mistakes. The researchers tested different team structures and found that the best one is like a pyramid, where each level checks what the other levels are doing. They also developed two ways to make the team more robust against bad agents: making each agent check what the others are saying, and adding an extra agent to review and fix mistakes. |
Keywords
» Artificial intelligence » Large language model » Translation
