用反学习技术追踪音乐生成模型的训练数据来源，实现版权溯源。
Large-Scale Training Data Attribution for Music Generative Models via Unlearning
- 通过反学习方法识别生成音乐时最关键的训练数据点。
- 在大规模文本到音乐扩散模型上验证了方法的有效性与一致性。
- 为音乐AI版权归属提供可解释的追溯方案，适合伦理与法律研究者。
本文探索在大规模数据集训练的音乐生成模型中，利用反学习方法进行训练数据溯源（TDA）。TDA旨在识别特定模型输出中贡献最大的训练数据点，这对人工智能生成音乐中的原创艺术家署名问题至关重要。通过白盒溯源，本工作支持更公平的艺术贡献认可，回应人工智能伦理与版权的紧迫关切。我们将在大规模文本到音乐扩散模型上应用反学习溯源，并通过网格搜索不同超参数配置，定量评估反学习方法的一致性。随后将反学习的溯源模式与非反事实方法进行对比。结果表明，反学习方法可有效适配音乐生成模型，首次引入大规模音乐生成领域的训练数据溯源，为更伦理、可问责的音乐生成AI系统铺平道路。
原文摘要 · Abstract (English)
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed the most to the generation of a particular output from a specific model. This is crucial in the context of AI-generated music, where proper recognition and credit for original artists are generally overlooked. By enabling white-box attribution, our work supports a fairer system for acknowledging artistic contributions and addresses pressing concerns related to AI ethics and copyright. We apply unlearning-based attribution to a text-to-music diffusion model trained on a large-scale dataset and investigate its feasibility and behavior in this setting. To validate the method, we perform a grid search over different hyperparameter configurations and quantitatively evaluate the consistency of the unlearning approach. We then compare attribution patterns from unlearning with non-counterfactual approaches. Our findings suggest that unlearning-based approaches can be effectively adapted to music generative models, introducing large-scale TDA to this domain and paving the way for more ethical and accountable AI systems for music creation.
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