English(EN) Getting a GRIP on Graph-Based Search for Learned Sparse Retrieval

新的GRIP系统挑战基于图的稀疏检索学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 20:33

研究人员开发了一种新的基于图的近似最近邻（ANN）系统，用于学习稀疏检索（LSR），名为GRIP。该系统挑战了倒排索引在LSR方面优于基于图的索引的普遍观点。GRIP采用了基于术语的入口点种子选择、通过稀疏草图进行的近似路由以及基于稀疏分区的图构建等技术来提高性能。在MS MARCO v2基准测试中，GRIP在LSR领先的倒排索引Seismic的基础上，延迟提高了2.5倍，同时保持了95%的recall@10。 AI

影响这项研究可能会改变部署学习稀疏检索系统的标准方法，从而带来更高效的搜索技术。

排序理由该集群包含一篇详细介绍新系统和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新系统和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Rossano Venturini · 2026-10-21 20:33

掌握基于图的搜索以实现学习稀疏检索

Learned sparse retrieval (LSR) has recently gained traction in the information retrieval community for the ability to combine the generalization capabilities of neural representations with the interpretability of keyword-based search. A number of recent works have demonstrated th…

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掌握基于图的搜索以实现学习稀疏检索

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