# BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering

Source: [BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering](<https://www.sota2.com/research/paper/68fa77f21e209131cea762a0>)

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

Data updated: 2025-10-26T04:29:27.078Z

Authors: Jie He, Simon Chi Lok U, V\\'ictor Guti\\'errez-Basulto, Jeff Z. Pan

Published: 2023-05-25T10:59:47.000Z

## Abstract

Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in their scope. A popular approach to UCR is to fine-tune language models with external knowledge (e.g., knowledge graphs), but this usually requires a large number of training examples. In this paper, we propose to transform the downstream multiple choice question answering task into a simpler binary classification task by ranking all candidate answers according to their reasonableness. To this end, for training the model, we convert the knowledge graph triples into reasonable and unreasonable texts. Extensive experimental results show the effectiveness of our approach on various multiple choice question answering benchmarks. Furthermore, compared with existing UCR approaches using KGs, ours is less data hungry. Our code is available at https://github.com/probe2/BUCA.

## 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/2305.15932>)

[PDF](<https://arxiv.org/pdf/2305.15932.pdf>)