--- title: "Dense Passage Retrieval for Open-Domain — Karpukhin 2020" description: "Open-domain question answering systems usually first pull a small set of candidate passages from a huge corpus, and then a reader model extracts the…" authors: ["Vladimir Karpukhin", "Barlas Oguz", "Sewon Min", "Patrick Lewis", "Ledell Wu", "Sergey Edunov", "Danqi Chen", "Wen-tau Yih"] year: 2020 venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)" source: "https://aclanthology.org/2020.emnlp-main.550/" doi: "10.18653/v1/2020.emnlp-main.550" arxiv: "2004.04906" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2020](https://md.research.elamur.ai/free/en/papers/2020/page/2/) / Dense Passage Retrieval for Open-Domain Question Answering # Dense Passage Retrieval for Open-Domain Question Answering **Authors:** [Vladimir Karpukhin](https://md.research.elamur.ai/free/en/authors/vladimir-karpukhin/), [Barlas Oguz](https://md.research.elamur.ai/free/en/authors/barlas-oguz/), [Sewon Min](https://md.research.elamur.ai/free/en/authors/sewon-min/), [Patrick Lewis](https://md.research.elamur.ai/free/en/authors/patrick-lewis/), [Ledell Wu](https://md.research.elamur.ai/free/en/authors/ledell-wu/), [Sergey Edunov](https://md.research.elamur.ai/free/en/authors/sergey-edunov/), [Danqi Chen](https://md.research.elamur.ai/free/en/authors/danqi-chen/), [Wen-tau Yih](https://md.research.elamur.ai/free/en/authors/wen-tau-yih/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.550/) · [PDF](https://aclanthology.org/2020.emnlp-main.550.pdf) · [DOI 10.18653/v1/2020.emnlp-main.550](https://doi.org/10.18653/v1/2020.emnlp-main.550) · [arXiv 2004.04906](https://arxiv.org/abs/2004.04906) · [OpenAlex](https://openalex.org/W3015883388) **Citations:** 3387 citations (openalex-snapshot · 2026-10-14) · [1982](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Open-domain question answering systems usually first pull a small set of candidate passages from a huge corpus, and then a reader model extracts the answer from them. Until this paper, that first step was dominated by sparse keyword methods such as TF-IDF and BM25. Earlier dense alternatives like ORQA needed costly extra pretraining. The authors show that this pretraining is unnecessary. Their Dense Passage Retriever (DPR) is a dual encoder: two separate BERT-base models embed questions and passages, and relevance is scored by dot product. It is trained only on existing question–passage pairs. The key training choice is to treat the gold passages of other questions in the same mini-batch as negatives, plus one hard negative per question taken from BM25. Passages from a 21-million-passage Wikipedia split are encoded ahead of time and searched with FAISS. Across several benchmarks, DPR clearly beats BM25 on retrieval. Paired with a standard reader, it sets new state-of-the-art end-to-end results. The work made dense retrieval a practical default for QA and retrieval-augmented generation. ## Key points - On top-20 passage retrieval accuracy, DPR beats a Lucene-BM25 baseline by 9–19 absolute points across open-domain QA datasets. On Natural Questions, top-5 accuracy is 65.2% for DPR vs. 42.9% for BM25. - End-to-end exact match on open Natural Questions is 41.5%, compared with 33.3% for ORQA. A jointly trained retriever–reader variant reached only 39.8 EM. - Setup: two independent BERT-base (uncased) encoders, using the 768-dimensional [CLS] vector. Training uses in-batch negatives plus one BM25 hard negative. The ablations found that other similarity functions gave no meaningful gain over the dot product. - The reader works best with about 50 retrieved passages on NQ. Using only 10 gives 40.8 EM, a small drop. Up to 100 passages still fit in one batch on a single 32GB GPU. - Code and trained models are available at github.com/facebookresearch/DPR. DPR was later used as the retriever in generation-based systems built on BART and T5. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Computing & GPUs](https://md.research.elamur.ai/free/en/shelves/computing/) ## Cited by Cited by 1982 papers of our library cite it (of the 140673 whose bibliographies we read); 106 of them have a page here, the first 100 below, 1876 not yet. - [Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/) - [Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks](https://md.research.elamur.ai/free/en/papers/asai-2022-evidentiality-guided-generation-knowledge-intensive/) - [Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/) - [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/) - 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BibTeX from the library's record of the work. ```bibtex @inproceedings{karpukhin2020dense, title = {{Dense Passage Retrieval for Open-Domain Question Answering}}, author = {Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2020}, doi = {10.18653/v1/2020.emnlp-main.550}, eprint = {2004.04906}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/2020.emnlp-main.550} } ``` - [← previous Denoising Diffusion Implicit Models](https://md.research.elamur.ai/free/en/papers/song-2020-denoising-diffusion-implicit-models/) - [next → Detecting Sarcasm in Conversation Context Using Transformer-Based Models](https://md.research.elamur.ai/free/en/papers/avvaru-2020-detecting-sarcasm-conversation-context-transformer/) - [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/2/) ## References 47 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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