Recent progress in information retrieval finds that embedding query and
document representation into multi-vector yields a robust bi-encoder retriever
on out-of-distribution datasets. In this paper, we explore whether late
interaction, the simplest form of multi-vector, is also helpful to neural
rerankers that only use the [CLS] vector to compute the similarity score.
Although intuitively, the attention mechanism of rerankers at the previous
layers already gathers the token-level information, we find adding late
interaction still brings an extra 5% improvement in average on
out-of-distribution datasets, with little increase in latency and no
degradation in in-domain effectiveness. Through extensive experiments and
analysis, we show that the finding is consistent across different model sizes
and first-stage retrievers of diverse natures and that the improvement is more
prominent on longer queries.

本文探讨了将后期交互用于神经重排器的有用性，以提高其在分布外数据集中的性能，该方法能带来额外的 5％改进，而无需增加太多的延迟和没有影响其在域内数据上的效果，通过实验和分析，发现这一结论与模型大小和不同类型的第一阶段检索器一致，并且这种改进在较长的查询上更为显著。