Retrieval Augmentation for T5 Re-ranker using External Sources
Kai Hui, Tao Chen, Qin, Zhen, Honglei Zhuang, Fernando G. Diaz, Mike Bendersky, Don Metzler · arXiv (Cornell University) · 2022
Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We investigate how to augment T5-based re-rankers using high-quality information retrieved from two external corpora -- a commercial web search engine and Wikipedia. We empirically demonstrate how retrieval augmentation can substantially improve the effectiveness of T5-based re-rankers for both in-domain and zero-shot out-of-domain re-ranking tasks.