Retrieval Augmentation for T5 Re-ranker using External Sources
This addresses the challenge of boosting re-ranking performance in information retrieval, though it appears incremental as it builds on existing retrieval augmentation methods.
The paper tackled the problem of whether retrieval augmentation can improve T5-based re-rankers by using external sources like web search and Wikipedia, and found it substantially enhances effectiveness for both in-domain and zero-shot out-of-domain tasks.
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.