In recent RAG approaches, rerankers play a pivotal role in refining retrieval accuracy with the ability of revealing logical relations for each pair of query and text. However, existing rerankers are required to repeatedly encode the query and a large number of long retrieved text. This results in high computational costs and limits the number of retrieved text, hindering accuracy. As a remedy of the problem, we introduce the Efficient Title Reranker via Broadcasting Query Encoder, a novel technique for title reranking that achieves a 20x-40x speedup over the vanilla passage reranker. Furthermore, we introduce Sigmoid Trick, a novel loss function customized for title reranking. Combining both techniques, we empirically validated their effectiveness, achieving state-of-the-art results on all four datasets we experimented with from the KILT knowledge benchmark.

翻译：在近期RAG方法中，重排器通过揭示查询与文本对的逻辑关系，在提升检索准确性方面发挥关键作用。然而，现有重排器需反复对查询及大量长检索文本进行编码，导致高昂的计算成本并限制了检索文本数量，从而影响准确性。针对该问题，我们提出基于广播查询编码器的高效标题重排器，这是一种新颖的标题重排技术，相较于传统段落重排器可实现20倍至40倍的速度提升。此外，我们引入Sigmoid Trick——一种专为标题重排定制的损失函数。通过结合两种技术，我们实证验证了其有效性，在KILT知识基准测试的全部四个数据集上均取得了最优结果。