Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates

TL;DR

This paper introduces GVIC, a multi-agent debate framework that improves value alignment in large language models by enabling agents to assess risks and communicate effectively, reducing harmful outputs and enhancing efficiency.

Contribution

The paper proposes GVIC, a novel multi-agent debate framework with gradual vigilance and interval communication, improving value alignment and reducing communication costs.

Findings

GVIC outperforms baseline methods in harmfulness mitigation.

GVIC is adaptable across different model sizes and task types.

Theoretically, GVIC optimizes debate efficiency and reduces communication overhead.

Abstract

In recent years, large language models have shown exceptional performance in fulfilling diverse human needs. However, their training data can introduce harmful content, underscoring the necessity for robust value alignment. Mainstream methods, which depend on feedback learning and supervised training, are resource-intensive and may constrain the full potential of the models. Multi-Agent Debate (MAD) offers a more efficient and innovative solution by enabling the generation of reliable answers through agent interactions. To apply MAD to value alignment, we examine the relationship between the helpfulness and harmlessness of debate outcomes and individual responses, and propose a MAD based framework Gradual Vigilance and Interval Communication (GVIC). GVIC allows agents to assess risks with varying levels of vigilance and to exchange diverse information through interval communication. We theoretically prove that GVIC 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. Additionally, GVIC exhibits strong adaptability across different base model sizes, including both unaligned and aligned models, and across various task types.