Paper ID: 2210.05145

Retrieval Augmentation for T5 Re-ranker using External Sources

Kai Hui, Tao Chen, Zhen Qin, Honglei Zhuang, Fernando Diaz, Mike Bendersky, Don Metzler

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.

Submitted: Oct 11, 2022