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 experiments reveal that indexing a corpus by fine-grained units such as propositions significantly outperforms passage-level units in retrieval tasks.Moreover, constructing prompts with fine-grained retrieved units for retrievalaugmented language models improves the performance of downstream QA tasks given a specific computation budget.

Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Pengshan Cai, Kaiqiang Song, Sangwoo Cho, Hongwei Wang, Xiaoyang Wang, Hong Yu, Fei Liu, Dong Yu. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.