Research
NLP
Dense Passage Retrieval for Open-Domain Question Answering
November 16, 2020
Abstract
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 greatly 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.
AUTHORS
Written by
Vladimir Karpukhin
Barlas Oguz
Sewon Min
Ledell Wu
Danqi Chen
Research Topics
Related Publications
September 24, 2026
Reinforcement Learni9ng
MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir, Jason Bohrer, Emily Shen, Jianfa Chen, Nan Jiang, Ankit Jain, Harihar Subramanyam, Mannat Singh, Chirag Nagpal
September 24, 2026
July 17, 2026
Conversational AI
Reinforcement Learni9ng
Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
Zilin Xiao, Qi Ma, Jason Chen, Xintao Chen, Avinash Atreya, Hanjie Chen, Vicente Ordonez
July 17, 2026
June 05, 2026
Conversational AI
Ranking & Recommendations
Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava
June 05, 2026
May 19, 2026
Human & Machine Intelligence
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales, Alvin W. M. Tan, Mahi Luthra, Charles-Eric Saint-James, Rashel Moritz, Sheila Krogh-Jespersen, Vanessa Stark, Surya Parimi, Jiayi Shen, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Tom Fizycki, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Juan Pino, Michael C. Frank, Emmanuel Dupoux
May 19, 2026
October 31, 2019
NLP
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
October 31, 2019
March 14, 2019
NLP
On the Pitfalls of Measuring Emergent Communication | Facebook AI Research
Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
March 14, 2019
January 13, 2020
NLP
Scaling up online speech recognition using ConvNets | Facebook AI Research
Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
January 13, 2020
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Meta © 2026
