Template Filling for Controllable Commonsense Reasoning

Large-scale sequence-to-sequence models have shown to be adept at both multiple-choice and open-domain commonsense reasoning tasks. However, the current formulations do not provide the ability to control the various attributes of the reasoning chain. To enable better controllability, we propose to study the commonsense reasoning as a template filling task (TemplateCSR) - where the language models fills reasoning templates with the given constraints as control factors. As an approach to TemplateCSR, we (i) propose a dataset of commonsense reasoning template-expansion pairs for healthcare and well-being domain and (ii) introduce ITO, an instruction fine-tuned sequence-to-sequence model that performs commonsense reasoning across concepts in the template. Our experiments show that our approach outperforms baseline both in generation metrics and factuality metrics. We also present a detailed error analysis on our approach's ability to reliably perform template based commonsense reasoning.