Moral values play a fundamental role in how we evaluate information, make decisions, and form judgements around important social issues. The possibility to extract morality rapidly from lyrics enables a deeper understanding of our music-listening behaviours. Building on the Moral Foundations Theory (MFT), we tasked a set of transformer-based language models (BERT) fine-tuned on 2,721 synthetic lyrics generated by a large language model (GPT-4) to detect moral values in 200 real music lyrics annotated by two experts.We evaluate their predictive capabilities against a series of baselines including out-of-domain (BERT fine-tuned on MFT-annotated social media texts) and zero-shot (GPT-4) classification. The proposed models yielded the best accuracy across experiments, with an average F1 weighted score of 0.8. This performance is, on average, 5% higher than out-of-domain and zero-shot models. When examining precision in binary classification, the proposed models perform on average 12% higher than the baselines.Our approach contributes to annotation-free and effective lyrics morality learning, and provides useful insights into the knowledge distillation of LLMs regarding moral expression in music, and the potential impact of these technologies on the creative industries and musical culture.

翻译：道德价值观在我们评估信息、做出决策以及围绕重要社会问题形成判断的过程中发挥着基础性作用。从歌词中快速提取道德内涵的可能性，使我们能够更深入地理解自身的音乐聆听行为。基于道德基础理论（MFT），我们利用一系列基于Transformer的语言模型（BERT）来完成检测任务，这些模型在由大型语言模型（GPT-4）生成的2,721条合成歌词上进行了微调，用于检测由两位专家标注的200条真实音乐歌词中的道德价值观。我们将其预测能力与一系列基线模型进行了评估，包括领域外模型（在MFT标注的社交媒体文本上微调的BERT）和零样本分类模型（GPT-4）。所提出的模型在所有实验中取得了最佳准确率，平均加权F1分数为0.8。这一性能平均比领域外模型和零样本模型高出5%。在考察二分类精确率时，所提出的模型平均比基线模型高出12%。我们的方法有助于实现无需标注且有效的歌词道德学习，并为理解大型语言模型在音乐道德表达方面的知识蒸馏，以及这些技术对创意产业和音乐文化的潜在影响提供了有益的见解。