SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection
Jiale Zhang, Jiaxiang Chen, Zhucong Li, Jie Ding, Kui Zhao, Zenglin Xu, Xin Pang, Yinghui Xu

TL;DR
SlimRAG introduces a lightweight, entity-aware retrieval framework for language models that improves accuracy and efficiency by avoiding complex graph structures and focusing on relevant entities and context.
Contribution
It proposes a novel, structure-free retrieval method using entity embeddings and a new metric RITU, outperforming graph-based methods in accuracy and efficiency.
Findings
Outperforms graph-based baselines in accuracy across QA benchmarks.
Reduces index size and retrieval overhead significantly.
Achieves higher RITU scores indicating more compact and relevant retrievals.
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…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Advanced Graph Neural Networks
