  The task of detecting moral values in text has significant implications in
various fields, including natural language processing, social sciences, and
ethical decision-making. Previously proposed supervised models often suffer
from overfitting, leading to hyper-specialized moral classifiers that struggle
to perform well on data from different domains. To address this issue, we
introduce novel systems that leverage abstract concepts and common-sense
knowledge acquired from Large Language Models and Natural Language Inference
models during previous stages of training on multiple data sources. By doing
so, we aim to develop versatile and robust methods for detecting moral values
in real-world scenarios. Our approach uses the GPT 3.5 model as a zero-shot
ready-made unsupervised multi-label classifier for moral values detection,
eliminating the need for explicit training on labeled data. We compare it with
a smaller NLI-based zero-shot model. The results show that the NLI approach
achieves competitive results compared to the Davinci model. Furthermore, we
conduct an in-depth investigation of the performance of supervised systems in
the context of cross-domain multi-label moral value detection. This involves
training supervised models on different domains to explore their effectiveness
in handling data from different sources and comparing their performance with
the unsupervised methods. Our contributions encompass a thorough analysis of
both supervised and unsupervised methodologies for cross-domain value
detection. We introduce the Davinci model as a state-of-the-art zero-shot
unsupervised moral values classifier, pushing the boundaries of moral value
detection without the need for explicit training on labeled data. Additionally,
we perform a comparative evaluation of our approach with the supervised models,
shedding light on their respective strengths and weaknesses.
