# Unsupervised Open-Domain Question Answering

Source: [Unsupervised Open-Domain Question Answering](<https://www.sota2.com/research/paper/68fa6ab01e209131cea29c59>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:09:35.928Z

Authors: Pengfei Zhu, Xiaoguang Li, Jian Li, Hai Zhao

Published: 2021-08-31T13:19:52.000Z

## Abstract

Open-domain Question Answering (ODQA) has achieved significant results in terms of supervised learning manner. However, data annotation cannot also be irresistible for its huge demand in an open domain. Though unsupervised QA or unsupervised Machine Reading Comprehension (MRC) has been tried more or less, unsupervised ODQA has not been touched according to our best knowledge. This paper thus pioneers the work of unsupervised ODQA by formally introducing the task and proposing a series of key data construction methods. Our exploration in this work inspiringly shows unsupervised ODQA can reach up to 86% performance of supervised ones.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.13817>)

[PDF](<https://arxiv.org/pdf/2108.13817.pdf>)