用对抗协作提升检索增强生成的准确性
Enhancing Retrieval Augmentation via Adversarial Collaboration
- 引入检测器与求解器对抗协作，动态优化检索结果
- 在多个垂直领域中显著提升检索准确率，超越现有方法
- 适合需要高可靠性知识检索的应用场景
检索增强生成（RAG）是领域专用大模型的常用方法，但常受'检索幻觉'困扰——微调模型无法识别劣质检索文档，导致性能下降。为此，我们提出对抗协作检索增强生成（AC-RAG）框架。该框架采用两个异构智能体：通用检测器负责发现知识缺口，领域专精求解器提供精准解答。在调解者引导下，二者进行对抗协作，检测器持续质疑求解器，推动问题逐层拆解与知识迭代优化。大量实验表明，AC-RAG显著提升检索准确率，在多个垂直领域均优于当前最优RAG方法。
原文摘要 · Abstract (English)
Retrieval-augmented Generation (RAG) is a prevalent approach for domain-specific LLMs, yet it is often plagued by "Retrieval Hallucinations"--a phenomenon where fine-tuned models fail to recognize and act upon poor-quality retrieved documents, thus undermining performance. To address this, we propose the Adversarial Collaboration RAG (AC-RAG) framework. AC-RAG employs two heterogeneous agents: a generalist Detector that identifies knowledge gaps, and a domain-specialized Resolver that provides precise solutions. Guided by a moderator, these agents engage in an adversarial collaboration, where the Detector's persistent questioning challenges the Resolver's expertise. This dynamic process allows for iterative problem dissection and refined knowledge retrieval. Extensive experiments show that AC-RAG significantly improves retrieval accuracy and outperforms state-of-the-art RAG methods across various vertical domains.
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