The landscape of information retrieval has broadened from search services to
a critical component in various advanced applications, where indexing
efficiency, cost-effectiveness, and freshness are increasingly important yet
remain less explored. To address these demands, we introduce Semi-parametric
Vocabulary Disentangled Retrieval (SVDR). SVDR is a novel semi-parametric
retrieval framework that supports two types of indexes: an embedding-based
index for high effectiveness, akin to existing neural retrieval methods; and a
binary token index that allows for quick and cost-effective setup, resembling
traditional term-based retrieval. In our evaluation on three open-domain
question answering benchmarks with the entire Wikipedia as the retrieval
corpus, SVDR consistently demonstrates superiority. It achieves a 3% higher
top-1 retrieval accuracy compared to the dense retriever DPR when using an
embedding-based index and an 9% higher top-1 accuracy compared to BM25 when
using a binary token index. Specifically, the adoption of a binary token index
reduces index preparation time from 30 GPU hours to just 2 CPU hours and
storage size from 31 GB to 2 GB, achieving a 90% reduction compared to an
embedding-based index.

信息检索的研究领域逐渐扩大，从搜索服务发展为各种高级应用的关键组成部分。为了提高检索效率、成本效益和实时性，本文介绍了一种名为 Semi-parametric Vocabulary Disentangled Retrieval（SVDR）的新型半参数化检索框架。在三个开放域问答基准测试中，使用整个维基百科语料库进行评估，SVDR 始终显示出卓越性能。它使用基于嵌入的索引在 top-1 检索准确率上比基于嵌入的检索方法 DPR 高出 3％，使用基于二进制令牌的索引在 top-1 准确率上比 BM25 高出 9％。采用二进制令牌索引的方式将索引准备时间从 30 个 GPU 小时减少到仅需 2 个 CPU 小时，存储空间从 31 GB 减少到 2 GB，实现了 90％的降低，相较于基于嵌入的索引。