SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection

• URL: http://arxiv.org/abs/2506.17288v1
• Date: Sun, 15 Jun 2025 15:36:17 GMT
• Title: SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection
• Authors: Jiale Zhang, Jiaxiang Chen, Zhucong Li, Jie Ding, Kui Zhao, Zenglin Xu, Xin Pang, Yinghui Xu,
• Abstract summary: SlimRAG is a lightweight framework for retrieval without graphs.<n>It replaces structure-heavy components with a simple yet effective entity-aware mechanism.<n> Experiments show that SlimRAG outperforms strong flat and graph-based baselines in accuracy.
• Score: 38.200971604630524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. https://github.com/continue-ai-company/SlimRAG

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