# Unsupervised Natural Question Answering with a Small Model

Source: [Unsupervised Natural Question Answering with a Small Model](<https://www.sota2.com/research/paper/68fa66ad1e209131cea11a69>)

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

Data updated: 2025-10-28T13:56:41.004Z

Authors: Martin Andrews, Sam Witteveen

Published: 2019-11-19T15:18:39.000Z

## Abstract

The recent (2019-02) demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large models. This short paper describes an architecture through which much smaller models can also answer such questions - by making use of 'raw' external knowledge. The contribution of this work is that the methods presented here rely on unsupervised learning techniques, complementing the unsupervised training of the Language Model. The goal of this line of research is to be able to add knowledge explicitly, without extensive training.

## 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/1911.08340>)

[PDF](<https://arxiv.org/pdf/1911.08340.pdf>)