Ghost in the Encoder: Decodable Artist Identity Representations in Lyrics-to-Song Generation
Text-to-song generation models can be prompted to imitate specific artists or regurgitate entire songs from their training data. Although these phenomena have been documented behaviorally on small datasets, little is known about the internal representations that may give rise to them. Prior interpretability work on generative audio has focused on locating semantic concepts such as genre or time signature within model activations. In this work, we show that a trained model can be probed for linearly decodable representations of artist identity from song lyrics alone, without any additional identifiers. Through a controlled case study of ACE-Step 1.5 spanning 2,000 songs across 100 artists,...
| Authors | Arhan Vohra, Choenden Kyirong, Laura Ibáñez-Martínez et al. |
|---|---|
| Primary category | cs.SD |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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