# Unsupervised multiple choices question answering via universal corpus

Source: [Unsupervised multiple choices question answering via universal corpus](<https://www.sota2.com/research/paper/68fa72521e209131cea559e1>)

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

Data updated: 2025-10-26T02:23:12.204Z

Authors: Qin Zhang, Hao Ge, Xiaojun Chen, Meng Fang

Published: 2024-02-27T09:10:28.000Z

## Abstract

Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In this paper, we propose a novel framework designed to generate synthetic MCQA data barely based on contexts from the universal domain without relying on any form of manual annotation. Possible answers are extracted and used to produce related questions, then we leverage both named entities (NE) and knowledge graphs to discover plausible distractors to form complete synthetic samples. Experiments on multiple MCQA datasets demonstrate the effectiveness of our method.

## 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/2402.17333>)

[PDF](<https://arxiv.org/pdf/2402.17333.pdf>)