Before revision: Recent approaches towards passage retrieval have successfully employed representations from pretrained Language Models(LMs) with large effectiveness gains. However, due to high computational cost those approaches are usually limited to re-ranking scenarios. The candidates in such a scenario are typically retrieved by scalable bag-of-words retrieval models such as BM25.
After revision: Many recent approaches towards neural information retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval model such as BM25.