TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models
TRUEMUSE is a controlled benchmark for evaluating how well existing black-box methods can attribute musical features in text-to-music generation. It fine-tunes three models on known attribution targets across four musical attributes—melody, timbre, artist style, and genre—to systematically test different retrieval and training strategies.