Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF.

翻译：密集检索在信息检索领域取得了显著进展，通过在单次检索过程中实现高有效性同时保持在线效率。然而，应用伪相关反馈以进一步提升检索有效性会导致在线延迟翻倍。针对这一挑战，本文提出了一种通过利用预生成的伪查询将伪相关反馈过程转移到离线的单次密集检索框架。因此，在线检索简化为与伪查询的单次匹配，从而提供更快速的在线检索。所提出方法的有效性在标准TREC DL和HARD数据集上进行了评估，结果显示出其潜力。我们的代码在https://github.com/Rosenberg37/OPRF公开可用。