Papers with large-scale passage retrieval benchmarks

1 papers
Query-as-context Pre-training for Dense Passage Retrieval (2023.emnlp-main)

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Challenge: Existing methods to improve passage retrieval performance by using context-supervised pre-training are weakly correlated.
Approach: They propose to use query-as-context pre-training to train passage-query pairs . they evaluate the pre-trained models on large-scale passage retrieval benchmarks .
Outcome: The proposed technique improves performance on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks.

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