Abstract
Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, traditional RAG systems struggle with large-scale, unstructured corpora where information is fragmented. Recent advances incorporate knowledge graphs to capture relational structures, enabling more comprehensive retrieval for complex, multi-hop reasoning tasks. However, existing graph-based RAG (GraphRAG) methods rely on unstable and costly relation extraction for graph construction, often producing noisy graphs with incorrect or inconsistent relations that degrade retrieval quality. In this paper, we revisit the pipeline of existing GraphRAG systems and propose LinearRAG (Linear Graph-based Retrieval-Augmented Generation), an efficient framework that enables reliable graph construction and precise passage retrieval. Specifically, LinearRAG constructs a relation-free hierarchical graph, termed Tri-Graph, using only lightweight entity extraction and semantic linking, avoiding unstable relation modeling. This new paradigm of graph construction scales linearly with corpus size and incurs no extra token consumption, providing an economical and reliable indexing of the original passages. For retrieval, LinearRAG adopts a two-stage strategy: (i) relevant entity activation via local semantic bridging, followed by (ii) passage retrieval through global importance aggregation. Extensive experiments on four datasets demonstrate that LinearRAG significantly outperforms baseline models.
Abstract (translated)
检索增强生成（RAG）广泛用于通过利用外部知识来减少大规模语言模型（LLMs）的幻觉问题。虽然对于简单的查询非常有效，但传统的RAG系统在处理大型、无结构化且信息碎片化的语料库时会遇到困难。最近的研究进展中，知识图谱被用来捕捉关系结构，从而能够为复杂的多跳推理任务提供更全面的信息检索。然而，现有的基于图的RAG（GraphRAG）方法依赖于不稳定的和昂贵的关系提取来进行图构建，通常会产生含有错误或不一致关系的嘈杂图，这会降低检索质量。 在本文中，我们重新审视了现有GraphRAG系统的流程，并提出了LinearRAG（基于线性图的检索增强生成），这是一种高效的框架，能够实现可靠的图构造和精确的信息段检索。具体来说，LinearRAG使用轻量级实体提取和语义链接来构建无关系依赖的层次化图，即Tri-Graph，从而避免了不稳定的模型关系建模。这种新的图形构建范式与原始文档集的规模呈线性增长，并且不会产生额外的标记消耗，提供了一种经济而可靠的索引方式。 在检索方面，LinearRAG采用两阶段策略：首先是通过局部语义桥接激活相关实体，其次是通过全局重要性聚合进行信息段检索。在四个数据集上的广泛实验表明，LinearRAG显著优于基线模型。
URL
https://arxiv.org/abs/2510.10114
