The Moral Gap of Large Language Models

MFT has found numerous aplications, including analysis of political ideology (Graham et al., 2009), environmental attitudes (Fein-berg and Willer, 2013), vaccine hesitancy (Amin et al., 2017), social "Everyone deserves equal access to healthcare regardless of income" Fairness "Respect your elders and follow traditional "Stand with our troops - they sacrifice everything for our freedom" Loyalty "Marriage is sacred and should be protected The advent of deep learning and particularly transformer architectures marked a significant advancement in moral content analysis. Hoover et al. (2020) first applied deep learning models to moral The recent proposal of applying LLMs to moral content categorization (Bulla et al., 2025) showed promise but suffered from methodological limitations.