Summary of Synthetic Lyrics Detection Across Languages and Genres, by Yanis Labrak et al.
Synthetic Lyrics Detection Across Languages and Genres
by Yanis Labrak, Markus Frohmann, Gabriel Meseguer-Brocal, Elena V. Epure
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the use of large language models (LLMs) for generating music content, specifically lyrics. While LLMs have improved creative processes for artists, they also raise concerns about copyright violations, consumer satisfaction, and content spamming. To address this gap, researchers curated a diverse dataset of real and synthetic lyrics from multiple languages, genres, and artists. The pipeline was validated using both human and automated methods. Existing synthetic text detection features were evaluated on this novel data type, and strategies were explored to adjust the best feature for lyrics using unsupervised adaptation. The study also investigated cross-lingual generalization, data scalability, robustness to language combinations, and genre novelty in a few-shot detection scenario. The results show promising results within language families and similar genres, but challenges persist with lyrics from languages exhibiting distinct semantic structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big computers can help musicians create music lyrics. These supercomputers are really good at making text, but they also raise concerns about copying songs, making sure people like the music, and preventing fake content from flooding the internet. To solve this problem, scientists put together a special collection of real and made-up song lyrics from different languages, genres, and famous artists. They tested their system with both human judges and computer programs. The researchers also tried out different ways to detect if the lyrics are fake or not, and looked at how well these methods work across different languages and music styles. |
Keywords
» Artificial intelligence » Few shot » Generalization » Unsupervised
