Positive and Risky Message Assessment for Music Products

TL;DR

This paper presents a new benchmark and a multi-task predictive model for evaluating positive and potentially harmful messages in music products, demonstrating improved performance and offering insights through case studies with Large Language Models.

Contribution

Introduces a multi-faceted benchmark and an efficient multi-task model with ordinality-enforcement for assessing music content messages, advancing the field of music content evaluation.

Findings

The proposed model outperforms task-specific alternatives.

It can assess multiple message aspects simultaneously.

Case studies with LLMs provide valuable insights.

Abstract

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.