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.

使用大规模语言模型和自然语言推理模型训练获得的抽象概念和常识知识，我们研发了多领域道德价值检测的多功能和强大方法，其中采用 GPT 3.5 模型作为基于零样本的无监督多标签道德价值分类器，消除了对标记数据的显式训练需求。与较小的基于自然语言推理的零样本模型相比，结果显示自然语言推理方法在性能上与 Davinci 模型不相上下。此外，我们对在不同领域训练监督模型以探索其在处理来自不同源的数据时的效果，并将其与无监督方法进行比较的性能进行了详细的调查分析。推广了一种无监督道德价值检测的最新零样本模型 Davinci，并与监督模型进行了比较评估，从而突破了道德价值检测的极限，无需显式训练标记数据，并揭示了各自的优势和劣势。