Summary of Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-agent Debates, by Rui Zou et al.
Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates
by Rui Zou, Mengqi Wei, Jintian Feng, Qian Wan, Jianwen Sun, Sannyuya Liu
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 In this paper, researchers aim to improve the efficiency and effectiveness of large language models while ensuring they are free from harmful content. Current methods rely on feedback learning and supervised training, which can be resource-intensive and limit the potential of these models. To address this challenge, the authors propose a Multi-Agent Debate (MAD) framework that enables the generation of reliable answers through agent interactions. The MAD-based Gradual Vigilance and Interval Communication (GVIC) framework allows agents to assess risks with varying levels of vigilance and exchange diverse information through interval communication. This approach optimizes debate efficiency while reducing communication overhead. Experimental results demonstrate that GVIC consistently outperforms baseline methods across various tasks and datasets, particularly excelling in harmfulness mitigation and fraud prevention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very smart computers that can help us with many things. However, they can also learn bad things if their training data contains harmful content. To solve this problem, scientists created a new method called Multi-Agent Debate (MAD). MAD allows different agents to interact and find reliable answers together. The authors of the paper propose using MAD in a framework called Gradual Vigilance and Interval Communication (GVIC). GVIC helps agents assess risks more accurately and communicate better with each other. This approach makes language models work more efficiently while reducing the risk of learning bad things. |
Keywords
» Artificial intelligence » Supervised
