Dense retrieval has become a prominent method to obtain relevant context or
world knowledge in open-domain NLP tasks. When we use a learned dense retriever
on a retrieval corpus at inference time, an often-overlooked design choice is
the retrieval unit in which the corpus is indexed, e.g. document, passage, or
sentence. We discover that the retrieval unit choice significantly impacts the
performance of both retrieval and downstream tasks. Distinct from the typical
approach of using passages or sentences, we introduce a novel retrieval unit,
proposition, for dense retrieval. Propositions are defined as atomic
expressions within text, each encapsulating a distinct factoid and presented in
a concise, self-contained natural language format. We conduct an empirical
comparison of different retrieval granularity. Our results reveal that
proposition-based retrieval significantly outperforms traditional passage or
sentence-based methods in dense retrieval. Moreover, retrieval by proposition
also enhances the performance of downstream QA tasks, since the retrieved texts
are more condensed with question-relevant information, reducing the need for
lengthy input tokens and minimizing the inclusion of extraneous, irrelevant
information.

密集检索是一种获取相关背景或世界知识的开放领域自然语言处理任务的显著方法，检索单元的选择显著影响检索和下游任务的性能，与传统的段落或句子方法相比，基于命题的检索在密集检索中明显优于传统的检索方法，并通过提供与问题相关的信息来改进下游问答任务的性能。