In this work, we introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products. We initiate by setting a multi-faceted, multi-task benchmark for music content assessment. Subsequently, we introduce an efficient multi-task predictive model fortified with ordinality-enforcement to address this challenge. Our findings reveal that the proposed method not only significantly outperforms robust task-specific alternatives but also possesses the capability to assess multiple aspects simultaneously. Furthermore, through detailed case studies, where we employed Large Language Models (LLMs) as surrogates for content assessment, we provide valuable insights to inform and guide future research on this topic. The code for dataset creation and model implementation is publicly available at https://github.com/RiTUAL-UH/music-message-assessment.

翻译：本文提出了一项开创性的研究挑战：评估音乐产品中的积极与潜在有害信息。我们首先构建了一个多层面、多任务的音乐内容评估基准数据集。随后，引入了一种带有序数约束的高效多任务预测模型来解决该挑战。研究结果表明，所提出的方法不仅显著优于强任务特定替代方案，还能同时评估多个维度。此外，通过详细的案例研究——采用大语言模型作为内容评估代理——我们为未来相关研究提供了有价值的见解与指导。数据集创建及模型实现代码已公开于 https://github.com/RiTUAL-UH/music-message-assessment。