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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.10182 |
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| _version_ | 1866916197023350784 |
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| author | Zhang, Yigeng Shafaei, Mahsa González, Fabio A. Solorio, Thamar |
| author_facet | Zhang, Yigeng Shafaei, Mahsa González, Fabio A. Solorio, Thamar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10182 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Positive and Risky Message Assessment for Music Products Zhang, Yigeng Shafaei, Mahsa González, Fabio A. Solorio, Thamar Computation and Language Artificial Intelligence 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. |
| title | Positive and Risky Message Assessment for Music Products |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2309.10182 |