通过语义引导缩小检索范围，让大模型更准更快地找对答案。
GuidedRAG: Semantic Steering of Retrieval-Augmented Generation
- 先用语义筛选知识库，再进行检索，减少无效搜索。
- 检索相关性提升14%-15.8%，精度损失降低27.4%，耗时降几个数量级。
- 适用于各类RAG模型，适合追求效率与准确性的研发者。
本文提出GuidedRAG，一种新型检索增强生成框架，引入专用选择阶段与检索过程中的语义引导。与现有先进RAG方法依赖日益复杂的检索结构不同，GuidedRAG在检索前通过语义约束知识库，使检索空间更贴合用户意图，同时大幅缩小搜索范围。实验表明，GuidedRAG可将检索相关性提升14.0-15.8%，缓解19.7-27.4%的检索精度损失，并使检索开销降低数个数量级。相关文本块在排名中更早出现，用户意图对齐度提高31.8-36.8%。此外，该方法在15种不同的RAG变体上均实现全覆盖，证明其广泛适用性。这些结果确立了语义引导与选择作为提升当前RAG性能的强大且通用的新范式。
原文摘要 · Abstract (English)
In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage and semantic steering during retrieval. In contrast to current state-of-the-art RAG approaches, which depend on increasingly complex retrieval and knowledge structures, GuidedRAG constrains the knowledge base using semantics before retrieval, aligning the retrieval space with user intent while substantially reducing the search space. Our evaluation shows that GuidedRAG improves retrieval relevance by 14.0-15.8%, mitigates a 19.7-27.4% loss in retrieval precision, and reduces retrieval overhead by orders of magnitude. Moreover, relevant chunks are consistently retrieved earlier in the ranking process, while alignment with user intent improves by 31.8-36.8%. We further show that GuidedRAG achieves full coverage across 15 diverse RAG variants, demonstrating generalizability across the literature. Together, these findings establish semantic steering and selections as a powerful and generalizable paradigm for improving the current state-of-the-art in RAG.
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