  We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank"
perspective, where we optimize re-ranking by truncating the retrieved list
(i.e., trim re-ranking candidates). RLT is crucial for re-ranking as it can
improve re-ranking efficiency by sending variable-length candidate lists to a
re-ranker on a per-query basis. It also has the potential to improve re-ranking
effectiveness. Despite its importance, there is limited research into applying
RLT methods to this new perspective. To address this research gap, we reproduce
existing RLT methods in the context of re-ranking, especially newly emerged
large language model (LLM)-based re-ranking. In particular, we examine to what
extent established findings on RLT for retrieval are generalizable to the
"retrieve-then-re-rank" setup from three perspectives: (i) assessing RLT
methods in the context of LLM-based re-ranking with lexical first-stage
retrieval, (ii) investigating the impact of different types of first-stage
retrievers on RLT methods, and (iii) investigating the impact of different
types of re-rankers on RLT methods. We perform experiments on the TREC 2019 and
2020 deep learning tracks, investigating 8 RLT methods for pipelines involving
3 retrievers and 2 re-rankers. We reach new insights into RLT methods in the
context of re-ranking.
