Abstract
Retrieval-Augmented Generation (RAG) enhances language models by incorporating external knowledge at inference time. However, graph-based RAG systems often suffer from structural overhead and imprecise retrieval: they require costly pipelines for entity linking and relation extraction, yet frequently return subgraphs filled with loosely related or tangential content. This stems from a fundamental flaw -- semantic similarity does not imply semantic relevance. We introduce SlimRAG, a lightweight framework for retrieval without graphs. SlimRAG replaces structure-heavy components with a simple yet effective entity-aware mechanism. At indexing time, it constructs a compact entity-to-chunk table based on semantic embeddings. At query time, it identifies salient entities, retrieves and scores associated chunks, and assembles a concise, contextually relevant input -- without graph traversal or edge construction. To quantify retrieval efficiency, we propose Relative Index Token Utilization (RITU), a metric measuring the compactness of retrieved content. Experiments across multiple QA benchmarks show that SlimRAG outperforms strong flat and graph-based baselines in accuracy while reducing index size and RITU (e.g., 16.31 vs. 56+), highlighting the value of structure-free, entity-centric context selection. The code will be released soon. this https URL
Abstract (translated)
检索增强生成（RAG）通过在推理时融合外部知识来提升语言模型的能力。然而，基于图的RAG系统往往遭受结构开销大和检索不精确的问题：它们需要昂贵的实体链接和关系提取管道，但常常返回包含松散相关或旁枝内容的子图。这一问题的根本原因在于语义相似性并不意味着语义相关性。我们引入了SlimRAG，这是一个无需使用图表的轻量级框架。SlimRAG用一个简单而有效的以实体为中心的方法替代了结构复杂的组件。在索引构建阶段，它基于语义嵌入构造了一个紧凑的实体到片段表。查询时，它识别关键实体、检索并评估相关片段，并组装出简洁且上下文相关的输入——无需图遍历或边生成。为了量化检索效率，我们提出了相对索引令牌利用率（RITU）这一衡量检索内容紧凑性的指标。跨多个问答基准的实验表明，SlimRAG在准确率上超越了强大的平面和基于图表的基础模型，并减少了索引大小及RITU值（例如16.31与56+），突显了无结构且以实体为中心的内容选择方法的价值。代码即将发布：[此链接](https://this-url.com)
URL
https://arxiv.org/abs/2506.17288
