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.

翻译：文本中道德价值观的检测任务在自然语言处理、社会科学和伦理决策等多个领域具有重要意义。先前提出的监督模型常存在过拟合问题，导致产生过度专业化的道德分类器，难以在不同领域数据上表现良好。为解决这一问题，我们引入了新颖的系统，这些系统利用从大型语言模型和自然语言推理模型在先前多数据源训练阶段获得的抽象概念和常识知识。通过这种方式，我们旨在开发适用于现实场景的通用且鲁棒的道德价值观检测方法。我们的方法使用GPT 3.5模型作为零样本即用的无监督多标签道德价值观分类器，无需在标注数据上进行显式训练。我们将其与较小的基于自然语言推理的零样本模型进行比较。结果表明，与Davinci模型相比，自然语言推理方法取得了具有竞争力的结果。此外，我们深入研究了监督系统在跨领域多标签道德价值观检测背景下的性能。这涉及在不同领域训练监督模型，以探索其处理不同来源数据的有效性，并将其性能与无监督方法进行比较。我们的贡献包括对跨领域价值观检测的监督和无监督方法进行全面分析。我们将Davinci模型作为最先进的零样本无监督道德价值观分类器引入，在无需标注数据显式训练的情况下推进了道德价值观检测的边界。此外，我们对所提方法与监督模型进行了比较评估，揭示了它们各自的优势和不足。