Open Theses

Commonsense Question Answering (CommonsenseQA) is a task that aims to select the best answer to a question based on common sense. A recent study [1] shows that by first utilizing knowledge eliciting demonstrations and generating knowledge from large pre-trained language models (PLMs), incorporating generated knowledge into QA systems can significantly improve the ability of QA models to commonsense questions. However, this approach has the following drawbacks: 1) demonstration examples are hand-picked, 2) demonstrations are data-specific, 3) quality of generated knowledge and final model performance is significantly impacted by how demonstrations are created. The goal of this thesis is to explore better, dataset-agnostic ways to generate knowledge for CommonsenseQA using prompts.