Automatic Knowledge Augmentation for Generative Commonsense Reasoning

TL;DR

This paper introduces a data-centric, model-agnostic approach that automatically augments knowledge to enhance generative commonsense reasoning in language models without changing their architecture.

Contribution

It proposes an automatic knowledge augmentation method that generates semi-golden sentences, improving reasoning capabilities without requiring architecture changes or human data labeling.

Findings

Enhanced generative commonsense reasoning performance

No architecture modifications needed

Automated knowledge augmentation improves data quality

Abstract

Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge. However, generative language models still struggle to provide outputs, and the training set does not contain patterns that are sufficient for generative commonsense reasoning. In this paper, we propose a data-centric method that uses automatic knowledge augmentation to extend commonsense knowledge using a machine knowledge generator. This method can generate semi-golden sentences that improve the generative commonsense reasoning of a language model without architecture modifications. Furthermore, this approach is a model-agnostic method and does not require human effort for data construction.