  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.
