Papers › Domain-matched Pre-training Tasks for Dense Retrieval

Domain-matched Pre-training Tasks for Dense Retrieval

28 Jul 2021Findings (NAACL) 2022 7arXiv:2107.13602archive 2025-07-28

Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad

Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information retrieval, where additional pre-training has so far failed to produce convincing results. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations made available by pushshift.io. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines.

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facebookresearch/dpr-scale officialmentioned on GitHubpytorch report

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Information RetrievalPassage RetrievalRetrieval

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Passage Retrieval Natural Questions DPR-PAQ Precision@100 89.22 #2 of 10 Archive leaderboard report
Passage Retrieval Natural Questions DPR-PAQ Precision@20 84.68 #2 of 10 Archive leaderboard report

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