Hermann Kroll


Contextualizing Language Models for Norms Diverging from Social Majority
Niklas Kiehne | Hermann Kroll | Wolf-Tilo Balke
Findings of the Association for Computational Linguistics: EMNLP 2022

To comprehensibly contextualize decisions, artificial systems in social situations need a high degree of awareness of the rules of conduct of human behavior. Especially transformer-based language models have recently been shown to exhibit some such awareness. But what if norms in some social setting do not adhere to or even blatantly deviate from the mainstream? In this paper, we introduce a novel mechanism based on deontic logic to allow for a flexible adaptation of individual norms by de-biasing training data sets and a task-reduction to textual entailment. Building on the popular ‘Moral Stories’ dataset we on the one hand highlight the intrinsic bias of current language models, on the other hand characterize the adaptability of pre-trained models to deviating norms in fine-tuning settings.