# Positive and Risky Message Assessment for Music Products

arXiv: https://arxiv.org/abs/2309.10182 · HTML: https://mlsanity.com/paper/2309.10182/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [From None to Severe: Predicting Severity in Movie Scripts](https://mlsanity.com/paper/2109.09276/) (2021)
- [Attending the Emotions to Detect Online Abusive Language](https://mlsanity.com/paper/1909.03100/) (2019)
- [Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations](https://mlsanity.com/paper/1912.02477/) (2019)

## Built on by (most cited first, 6 of 6)

- [AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning](https://mlsanity.com/paper/2406.18060/) (2024)
- [Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity](https://mlsanity.com/paper/2507.23121/) (2025)
- [FUTURE: Flexible Unlearning for Tree Ensemble](https://mlsanity.com/paper/2508.21181/) (2025)
- [Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Model](https://mlsanity.com/paper/2406.07841/) (2024)
- [Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention](https://mlsanity.com/paper/2503.08079/) (2025)
- [Towards an LLM-based method for quantifying the sexual content in song lyrics](https://mlsanity.com/paper/2608.08885/) (2026)

## Abstract (arXiv)

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.