# Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge

Source: [Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge](<https://www.sota2.com/research/paper/68fa6b2d1e209131cea2c9ac>)

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

Data updated: 2025-10-25T23:42:15.448Z

Authors: Chi-Liang Liu, Hung-yi Lee

Published: 2020-10-21T13:44:35.000Z

## Abstract

In this paper, we study the possibility of almost unsupervised Multiple Choices Question Answering (MCQA). Starting from very basic knowledge, MCQA model knows that some choices have higher probabilities of being correct than the others. The information, though very noisy, guides the training of an MCQA model. The proposed method is shown to outperform the baseline approaches on RACE and even comparable with some supervised learning approaches on MC500.

## 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/2010.11003>)

[PDF](<https://arxiv.org/pdf/2010.11003.pdf>)