This paper addresses the need for improved precision in existing Retrieval-Augmented Generation (RAG) methods that primarily focus on enhancing recall. We propose a multi-layer knowledge pyramid approach within the RAG framework to achieve a better balance between precision and recall. The knowledge pyramid consists of three layers: Ontologies, Knowledge Graphs (KGs), and chunk-based raw text. We employ cross-layer augmentation techniques for comprehensive knowledge coverage and dynamic updates of the Ontology schema and instances. To ensure compactness, we utilize cross-layer filtering methods for knowledge condensation in KGs. Our approach, named PolyRAG, follows a waterfall model for retrieval, starting from the top of the pyramid and progressing down until a confident answer is obtained. We introduce two benchmarks for domain-specific knowledge retrieval, one in the academic domain and the other in the financial domain. The effectiveness of the methods has been validated through comprehensive experiments by outperforming 19 SOTA methods. An encouraging observation is that the proposed method has augmented the GPT-4, providing 395\% F1 gain by improving its performance from 0.1636 to 0.8109.

翻译：本文针对现有检索增强生成方法主要关注提升召回率而精度不足的问题，提出在RAG框架中采用多层知识金字塔结构，以实现精度与召回率的更好平衡。该知识金字塔包含三个层级：本体层、知识图谱层和基于文本块的原始文本层。我们采用跨层增强技术实现知识的全面覆盖，并支持本体模式与实例的动态更新。为确保结构紧凑性，我们利用跨层过滤方法对知识图谱进行知识浓缩。我们提出的PolyRAG方法采用瀑布模型进行检索，从金字塔顶端开始逐层向下检索，直至获得可靠答案。我们构建了两个领域特定知识检索基准数据集，分别面向学术领域和金融领域。通过全面实验验证，本方法在超越19种前沿方法的同时证明了其有效性。值得关注的发现是，所提方法显著增强了GPT-4的性能，通过将其F1分数从0.1636提升至0.8109，实现了395%的性能增益。