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Retrieval Augmentation for T5 Re-ranker using External Sources

arXiv 2210.05145 ↗First posted 11 Oct 2022cs.IR cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 11 Oct 2022.

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22 of 30 references.

References · 22

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10 more
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  1. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11