Replaces term matching with a dual-encoder that embeds questions and passages into the same vector space. Relevance becomes a nearest-neighbour lookup, which lets retrieval find passages that share no vocabulary with the question.
@inproceedings{karpukhin2020dense,
author = {Vladimir Karpukhin and Barlas Oguz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
title = {Dense Passage Retrieval for Open-Domain QA},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
year = {2020},
eprint = {2004.04906},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2004.04906}
}Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.
2023
The single most useful result for anyone shipping retrieval. A bigger context window does not fix relevance: if the right chunk lands in the middle of twenty, it may as well not be there. Re-rank and put the best passage last.
2021
Run this before you commit to a retriever. A model that wins on one corpus and collapses on the next is the most common way a search feature disappoints after launch.
2020
The paper behind most of what we ship. The practical appeal is control: knowledge lives in documents you own, so it can be corrected, versioned and audited.