Papers with Pairwise Relevance Distillation

1 papers
PairDistill: Pairwise Relevance Distillation for Dense Retrieval (2024.emnlp-main)

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Challenge: Recent advances in dense retrieval have demonstrated remarkable efficacy compared to traditional sparse retrieval methods.
Approach: They propose to use pairwise relevance distillation to leverage pairwise reranking to enrich the training of dense retrieval models.
Outcome: The proposed method outperforms existing methods and achieves state-of-the-art results on multiple benchmarks.

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