Apollo's Oracle: Retrieval-Augmented Reasoning in Multi-Agent Debates
Haotian Wang Xiyuan Du Weijiang Yu Qianglong Chen Kun Zhu Zheng Chu Lian Yan Yi Guan
Multi-agent debate systems are designed to derive accurate and consistent
conclusions through adversarial interactions among agents. However, these
systems often encounter challenges due to cognitive constraints, manifesting as
(1) agents' obstinate adherence to incorrect viewpoints and (2) their
propensity to abandon correct viewpoints. These issues are primarily
responsible for the ineffectiveness of such debates. Addressing the challenge
of cognitive constraints, we introduce a novel framework, the Multi-Agent
Debate with Retrieval Augmented (MADRA). MADRA incorporates retrieval of prior
knowledge into the debate process, effectively breaking cognitive constraints
and enhancing the agents' reasoning capabilities. Furthermore, we have
developed a self-selection module within this framework, enabling agents to
autonomously select pertinent evidence, thereby minimizing the impact of
irrelevant or noisy data. We have comprehensively tested and analyzed MADRA
across six diverse datasets. The experimental results demonstrate that our
approach significantly enhances performance across various tasks, proving the
effectiveness of our proposed method.