Papers › Dense Passage Retrieval for Open-Domain Question Answering
Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.
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Code
Syntology Ran 10 of 14 code samples harvested from 10 repositories linked to this paper; 4 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 9 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Passage Retrieval | Natural Questions | DPR | Precision@100 | 86 | #8 of 10 | Archive leaderboard | report |
| Passage Retrieval | Natural Questions | DPR | Precision@20 | 79.4 | #8 of 10 | Archive leaderboard | report |
| Question Answering | Natural Questions | DPR | EM | 41.5 | #23 of 47 | Archive leaderboard | report |
| Question Answering | NaturalQA | DPR | EM | 41.5 | #1 of 4 | Archive leaderboard | report |
| Question Answering | TriviaQA | DPR | EM | 56.8 | #41 of 56 | Archive leaderboard | report |
| Question Answering | WebQuestions | DPR | EM | 42.4 | #17 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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