渐进式警觉与区间通信：增强多智能体辩论中的价值对齐
Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates
AI总结：
本文提出基于多智能体辩论的框架GVIC，通过渐进式警觉和区间通信增强价值对齐，理论证明其优化辩论效率并降低通信开销，实验表明其在有害性缓解和欺诈预防上优于基线，且适应不同模型规模和任务类型。
摘要：
近年来，大型语言模型在满足人类多样化需求方面表现出色。然而，其训练数据可能引入有害内容，凸显了鲁棒价值对齐的必要性。主流方法依赖反馈学习和监督训练，资源消耗大且可能限制模型的全部潜力。多智能体辩论（MAD）通过智能体交互生成可靠答案，提供了一种更高效、创新的解决方案。为将MAD应用于价值对齐，我们研究了辩论结果与个体响应在有益性和无害性之间的关系，并提出基于MAD的框架——渐进式警觉与区间通信（GVIC）。GVIC允许智能体以不同警觉级别评估风险，并通过区间通信交换多样化信息。我们理论上证明，GVIC在优化辩论效率的同时降低了通信开销。实验结果表明，GVIC在多种任务和数据集上始终优于基线方法，尤其在缓解有害性和预防欺诈方面表现突出。此外，GVIC在不同基础模型规模（包括未对齐和对齐模型）及多种任务类型上均展现出强适应性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
