Probing Power by Prompting: Harnessing Pre-trained Language Models for Power Connotation Framing

Subtle changes in word choice in communication can evoke very different associations with the involved actors. For instance, a company ' employing workers ' evokes a more positive connotation than the one ' exploiting ' them. This concept is called connotation. This paper investigates whether pre-trained language models (PLMs) encode such subtle connotative information about power differentials between involved entities. We design a probing framework for power connotation, building on Sap et al. (2017)' s operationalization of connotation frames. We show that zero-shot prompting of PLMs leads to above chance prediction of power connotation, however fine-tuning PLMs using our framework drastically improves their accuracy. Using our fine-tuned models, we present a case study of power dynamics in US news reporting on immigration, showing the potential of our framework as a tool for understanding subtle bias in the media.